Scientific/Technical/Management - NCAR Research ... · Web viewAnother important factor to Ae....

ROSES 2013 <NIP>NRA NNH13ZDA001N <Global Modeling of Containers for Dengue Mosquito>

111Equation Chapter 1 Section 1Contents

1 SCIENTIFIC/TECHNICAL/MANAGEMENT...................................................................1-11.1 Objectives and Expected Significance...........................................................................1-4

1.1.1 Objectives...............................................................................................................1-41.1.2 Expected Significance............................................................................................1-4

1.2 Technical Approach and Methodology.........................................................................1-51.3 Perceived Impact to State of Knowledge.....................................................................1-131.4 Relevance to Element Programs and Objectives in the NRA......................................1-131.5 Work Plan....................................................................................................................1-14

1.5.1 Key Milestones.....................................................................................................1-151.5.2 Management Structure.........................................................................................1-151.5.3 Contributions of Principal Investigator................................................................1-151.5.4 Collaborators........................................................................................................1-15

2 REFERENCES AND CITATIONS.....................................................................................2-13 BIOGRAPHICAL SKETCH................................................................................................3-1

3.1 Principal Investigator.....................................................................................................3-14 CURRENT AND PENDING SUPPORT.............................................................................4-1

4.1 Current Awards..............................................................................................................4-14.2 Pending Awards.............................................................................................................4-1

5 BUDGET JUSTIFICATION: NARRATIVE AND DETAILS...........................................5-15.1 Budget Narrative............................................................................................................5-1

5.1.1 Personnel and Work Effort.....................................................................................5-15.1.2 Facilities and Equipment........................................................................................5-1

5.2 Budget Details...............................................................................................................5-1

Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.


1 Scientific/Technical/ManagementObjective Summary:

We propose to employ NASA Earth Science products to develop a framework for global application of a recently-developed energy balance model that estimates water temperature and height in artificial containers that serve as habitat for the immature life stages of the dengue vector mosquito Aedes aegypti. The simulations will allow us to produce, with a physically-based approach, global suitability maps for the development of Ae. aegypti. The energy balance model will then be coupled with a dynamic life cycle model to best describe the seasonality and interannual variability of Ae. aegypti population dynamics for locations in North America and the Caribbean where we have field data at several locations for refining and validating our simulations. Finally, the sensitivity of results to climate change scenarios will be explored.

Motivation and Background:Dengue is the most common and important vector-borne virus in the world (WHO 2009),

with 3.5 billion people in over 100 countries living in regions of high risk (Guha-Sapir and Schimmer 2005; Kroeger and Nathan 2006; Beatty et al. 2009). Dengue risk areas (Fig. 1-1) extend across the tropical and sub-tropical Americas and Africa, southeast Asia, India, and Oceana (Guzman and Kouri 2002; Renganathan et al. 2003). A recent study indicates that dengue infections, once thought to number 50-200 million per year (Gubler 1998, 2004; Beatty et al. 2009; WHO 2009), actually total about 390 million per year (Bhatt et al. 2013), with about 1% of cases exhibiting the severe and often deadly dengue hemorrhagic fever (Gubler 1998). Both the geographic range and the magnitude of dengue infections have increased in the past 50 years (WHO 2009), due to population growth and urbanization in endemic areas, increases in global mobility and trade (Westaway and Blok 1997), and the discontinuation of insecticide spraying programs (particularly in the Americas) because of financial and environmental concerns (Gubler 1989). Despite the almost nonexistent risk for dengue in the United States in Fig. 1-1, dengue outbreaks have recently occurred in Key West FL (Graham et al. 2011; Radke et al. 2012), and dengue is present in the U.S./Mexico border region (e.g., Ramos et al. 2008; Hotez et al. 2012), with low income, absence of air conditioning, non-functioning window screens, and inadequate sanitation being strong risk factors (Reiter et al. 2003; Brunkard et al. 2007). An estimated 100,000-200,000 dengue cases occur annually among the Mexican-American population in the United States, presumably near the U.S./Mexico border (Hotez 2008). Aedes aegypti populations extend well into the United States, including southern Arizona (Engelthaler et al. 1997; Hoeck et al. 2003; Merrill et al. 2005; Hayden et al. 2010), and historically the mosquito’s range has extended up the U.S. eastern seaboard, being responsible for the 1793 Yellow Fever outbreak in Philadelphia (Foster et al. 1998).

1Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.

Figure 1-1. National and subnational evidence consensus on complete absence (green) to complete presence (red)

of dengue, following Bhatt et al. (2013).


The primary dengue vector mosquito Aedes aegypti is closely associated with humans. It lives exclusively in urban and semi-urban areas, preferentially bites humans, and spends its developmental stages in artificial water containers (Focks and Alexander 2006; Halstead 2008; Scott et al. 1993). Ae. aegypti population dynamics thus depend on human behavior and socio-economic factors, including infrastructure, solid waste disposal, housing characteristics, and transportation (Chang et al. 1997; Gubler 1997; Nagao et al. 2003; Kay and Vu 2005; Morrison et al. 2008; Hayden et al. 2010). Another important factor to Ae. aegypti survival and development is climate variability. The primary limiting climatic factor for survival is cold temperature, with an approximate lower boundary of 10°C average winter temperature, below which Ae. aegypti are not observed (Christophers 1960; Focks et al. 1993a; Hopp and Foley 2001; Farnesi et al. 2009; Yang et al. 2009; Richardson et al. 2011). Water availability is a limiting factor in arid regions, and the seasonality of Ae. aegpyti depends on temperature in subtropical regions and rainy seasons (water availability) in tropical regions.

Climate effects extend to development of Aedes aegypti immature mosquitoes in artificial containers. Potential containers for Ae. aegypti immature development include, but are not limited to, small sundry items (e.g., bottles, cans, plastic containers), buckets, tires, barrels, tanks, and cisterns (Morrison et al. 2004; Tun-Lin et al. 2009; Bartlett-Healy et al. 2012). Successful development of immature mosquitoes from eggs to larvae, pupae, and eventually adults is largely dependent on the availability of water and the thermal properties of the water in the containers. The optimal temperature for Ae. aegypti larval and pupal development, with short development times and high survival rates, is in the range of 24°-34°C (Bar-Zeev 1958; Rueda et al. 1990; Tun-Lin et al. 2000; Kamimura et al. 2002; Mohammed and Chadee 2011; Padmanabha et al. 2011a, 2012; Richardson et al. 2011; Farjana et al. 2012). Larval development can be impeded by water temperatures that are too low (8°-12°C) or high enough to cause physical harm, through heat stress (36°-44°C) (Bar-Zeev 1958, Smith et al. 1988, Tun-Lin et al. 2000, Kamimura et al. 2002, Chang et al. 2007, Richardson et al. 2011, Muturi et al. 2012). There also is a growing recognition that the magnitude of the daily temperature range (i.e., fluctuations over the course of a 24-hour period) impact life history traits of Ae. aegypti, including larval development time (Lambrechts et al. 2011, Mohammed and Chadee 2011, Carrington et al. 2013). Other factors that can have negative effects on larval development time or survival include poor nutrient content of the water and resource competition (Braks et al. 2004, Juliano et al. 2004, Padmanabha et al. 2011b, Walsh et al. 2011). For rain-filled containers, there are also risks of a container drying out or of the container over-flowing and the immatures being flushed out (Koenraadt and Harrington 2008, Bartlett-Healy et al. 2011).

Mathematical modeling of mosquito populations is done through statistical methods (i.e., regression or generalized models) or through dynamic life cycle models that simulate the life cycles of cohorts of mosquitoes using a mechanistic approach. Dynamic life cycle models for Aedes aegypti, including CIMSiM (Container Inhabiting Mosquito Simulation Model) and Skeeter Buster, are strongly influenced by water temperature, which impacts the development times and survival rates of eggs, larvae and pupae (Focks et al. 1993a,b; Cheng et al. 1998; Magori et al. 2009; Ellis et al. 2011). Perhaps the greatest limitation of these complex simulation models is the continued use of simplistic empirical relationships to predict water temperature in and water loss from containers based on several meteorological variables. Recent work has shown that physics-based approaches toward modeling container water properties are promising for resolving the complexities of container water dynamics (Tarakidzwa 1997, Kearney et al. 2009). Such models solve for the energy balance of the water inside of the container, taking into



account shortwave (solar) and longwave (terrestrial) radiation and heat fluxes (sensible, latent, and ground). The methodology is similar to what is done in land surface models to simulate ground surface and soil temperature (e.g., Chen and Dudhia 2001), except modified for container dimensions. An energy balance container model developed by the proposer, termed the Water Height And Temperature in Container Habitats Energy Model (WHATCH’EM; Steinhoff and Monaghan 2013), solves for water temperature and height for user-specified containers with readily available weather data. Realistic estimation of water temperature and height from WHATCH’EM has potential to improve output from mosquito population models.

While sufficient climate data is available for many tropical and sub-tropical urban areas endemic to dengue, this is not necessarily the case in regions of Africa and southeast Asia. Additionally, rapid urbanization, inadequate infrastructure, and poor quality housing in these regions, along with favorable climate, result in high dengue risk. African dengue risk in particular is often underestimated, due to symptomatically similar illnesses and underreporting (Bhatt et al. 2013). Even in regions with high-quality climate data, variables like cloud cover and ground temperature, important for the surface energy balance, are localized and difficult to directly observe and apply spatially. Satellite remote sensing products and gridded numerical weather prediction products (including atmospheric reanalyses), which utilize data from a variety of sources, offer high quality estimate of climate data in regions where in situ climate data is insufficient. Similarly, gridded global climate model (GCM) output provides physically-based estimates of future climate states, and relatively simple methods exist to downscale coarse resolution GCM output for regional applications.

Given the global importance of dengue, and the strong influence of climate and water temperature to the dengue vector mosquito Aedes aegypti, we propose to model habitat suitability globally by creating a framework that uses high quality gridded NASA Earth Science products (Table 1-1), based on container water temperature and availability estimates from WHATCH’EM. The number of mosquitoes produced by containers (e.g., productivity) of different types and shading scenarios can be determined geographically. We will then couple the NASA/WHATCH’EM framework with the mosquito population model Skeeter Buster to characterize the seasonality and interannual variability of mosquito population dynamics for several locations across North America and the Caribbean where we have detailed data on container distributions and pupal counts. Current climate conditions will then be perturbed, using established methodologies, based on future climate scenarios derived from a NASA GCM (Table 1-1) to determine the sensitivity and response of habitat suitability and mosquito population dynamics to climate change.

NASA Earth Science Product Variables Used Grid Spacing Time Periods Used

MERRA 2 m air temperature, 2 m specific humidity, 2 m wind speed 0.5° lat, 0.67° lon 1979-present

GLDAS Ground surface temperature, Soil temperature 0.25° lat, 0.25° lon 2009-presentNLDAS Ground surface temperature, Soil temperature 0.125° lat, 0.125° lon 1979-presentTRMM Precipitation 0.25° lat, 0.25° lon 1999-present

NASA Langley Cloud Cover Cloud fraction 0.25° lat, 0.3125° lon 2009-present

GISS-E2-R 2 m air temperature, 2 m specific humidity, Precipitation 2.0° lat, 2.5° lon 1979-present, 2020-2035,

2060-2075Table 1-1. NASA Earth Science products used in this study.



1.1 Objectives and Expected Significance1.1.1 ObjectivesThe objectives of this research are the following:

1) Create a framework for global application of WHATCH’EM that employs NASA Earth Science products

2) Refine the NASA-based WHATCH’EM system by running the model for 2013 boreal summer and comparing direct observations of container temperatures and WHATCH’EM simulations using in situ weather data as input. Assess model biases/errors and improve the model as needed. Conduct additional container experiments in Boulder, Colorado as needed to address unforeseen situations and weaknesses in WHATCH'EM.

3) Generate global Aedes aegypti habitat suitability maps for a variety of scenarios for shading and container types and characteristics.

4) Couple WHATCH’EM with Skeeter Buster for several areas in North America and the Caribbean with detailed container distribution data and pupal counts, to ascertain the seasonality and interannual variability of Aedes aegypti population dynamics.

5) Assess climate change scenarios for habitat suitability and Aedes aegypti population dynamics.

1.1.2 Expected SignificanceThis research has three broad areas of significance. First, the proposed work will develop

and apply a framework for assessing habitat suitability for the dengue virus vector mosquito Aedes aegypti for present day and future scenarios. NASA products will solely be used as weather-related input data to construct the container suitability maps – MERRA, GLDAS/NLDAS, TRMM, and cloud cover products. Productivity of different types of containers and shading scenarios can be assessed and be instrumental in mosquito control efforts. Mosquito population dynamics will be studied for select areas of North America and the Caribbean, where the coupled WHATCH’EM-Skeeter Buster model will provide estimates of interannual variability and seasonality of mosquito populations. Changes to the range and seasonality of the mosquito for these locations can then be examined with climate change scenarios.

Second, this work will improve open-access community research models WHATCH’EM and Skeeter Buster. Specific proposed improvements to WHATCH’EM include parameterizing surface temperature and air temperature near container height based on ground temperature, 2 m air temperature, wind speed, and the ground surface characteristics, estimating the emitting temperature of shading surfaces, and representing thermal stratification in large containers. Mosquito population estimates from Skeeter Buster will be improved using physically-based water temperature and water height estimates from WHATCH’EM rather than the default regression-based calculations.

Third, the proposed work will extend the capability of WHATCH’EM to larger-scale applications. The model was developed to estimate container suitability for an NSF-funded project in eastern Mexico. It is now being used in its current form for a Defense Threat Reduction Agency (DTRA) project to develop a prototype real-time dengue surveillance system utilizing extremely high resolution visible satellite imagery to identify containers in select locations. The currently-proposed project represents the next step in the development of WHATCH'EM: a framework will be developed to apply WHATCH’EM globally, using NASA



Earth science products. WHATCH'EM will then be coupled with Skeeter Buster (to provide the linkage between suitable container habitats and Aedes aegypti population dynamics). Finally, simulations will be performed to explore Ae. aegypti habitat suitability and population dynamics for present-day and future scenarios. The WHATCH’EM product run with NASA Earth Science products could also potentially be run in near-real-time for dengue surveillance applications, and for example would be an excellent application for NASA's SERVIR platform for decision makers.

1.2 Technical Approach and MethodologyOverview

We propose to generate and apply a globally-applicable framework to assess habitat suitability and population dynamics for the dengue virus vector mosquito Aedes aegypti by adapting the container energy balance model WHATCH’EM to use gridded NASA Earth Science datasets (MERRA, GLDAS/NLDAS, TRMM, and cloud cover), and coupling WHATCH’EM with the mosquito population model Skeeter Buster. From these models and the gridded NASA input products, we will create 1) global Ae. aegypti suitability maps based on water temperature and water availability, and 2) targeted estimates of Ae. aegypti population dynamics (seasonality and interannual variability) for select areas of North America and the Caribbean. Suitability maps and population estimates will be created for both present day conditions and future climate scenarios. The datasets and models to be used in this work are now described in turn.

Description of Data SetsA.1 MERRA

The Modern Era Retrospective-Analysis for Research and Applications (MERRA; Rienecker et al. 2011) is produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA integrates conventional meteorological observation data and satellite radiances and retrievals with a temporally and spatially consistent numerical modeling system to produce a gridded dataset suitable for analysis of climate variability. MERRA implements a 3DVAR assimilation system; a variational bias correction of satellite radiances; and the Incremental Analysis Updates (IAU), a nudging technique allowing for a smooth transition from the model states toward the observed state (Rienecker et al. 2008; Cullather and Bosilovich 2012). MERRA is available from the start of the modern satellite era (1979) through the present, at 0.5° latitude by 0.67° longitude grid spacing and with 72 layers in the vertical. Output is available at hourly intervals from the Goddard Earth Sciences Data Information Services Center (GES DISC). MERRA variables that will be used as input to the container model include temperature at 2 m above displacement height, wind speed at 2 m above displacement height, and specific humidity at 2 m above displacement height. Cloud cover, precipitation, ground surface temperature, and soil temperature will be utilized from MERRA for longer simulations where other NASA Earth science products are not available. Validation studies show that MERRA annually overestimates (underestimates) rainfall over Africa, India, and the western tropical Pacific (South America) compared to the Global Precipitation Climatology Project (GPCP, Adler et al. 2003), but at levels similar to other reanalyses (Bosilovich et al. 2008; Lorenz and Kunstmann 2012). The same validation studies show correlations between MERRA and GPCP and other rainfall datasets at 0.7 or above for most regions for mean annual precipitation and the



multi-year monthly precipitation cycle. Lorenz and Kunstmann (2012) find 2 m temperature biases of less than 4°C over most of the tropics.

A.2 GLDAS and NLDASThe Global Land Data Assimilation System (GLDAS; Rodell et al. 2004) utilizes satellite-

and ground-based observations and the Land Information System (LIS; Kumar et al. 2006) to run a suite of offline land surface models for optimal estimation of ground surface and soil conditions. GLDAS version 1 output is available at 0.25° grid spacing globally north of 60°S at 3-hourly intervals (a 15 minute model time step is used) for 2000-present near-real time, with output utilizing the four layer National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (Noah) land surface model (Chen et al. 1996; Koren et al. 1999). The North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) is similar to GLDAS but specific to North America. NLDAS version 2 output is available at 0.125° grid spacing at 1-hour intervals for 1979-present near-real time.

GLDAS and NLDAS output that will be used in this work includes ground surface (skin) temperature, to represent terrestrial radiation emitted to the container from the ground, and temperature of the uppermost soil layer (top 10 cm), to estimate ground heat flux to/from the container. A global validation of GLDAS was performed by Zaitchik et al. (2010), who used a source-to-sink (STS) river routing scheme to simulate river discharge estimates for comparison with observations across major rivers around the world. For tropical rivers, GLDAS simulations using Noah generally perform better than simulations with other land surface models, with reasonable estimations of the timing of peak discharge and seasonal variability in South America, Africa, and Asia. Discrepancies were largely tied to deficiencies in input rainfall datasets, especially in regions of Africa with poor precipitation records. Xia et al. (2013) validated Noah-simulated NLDAS soil temperature estimates against observations across the United States, finding general agreement, especially for upper soil layers.

A.3 TRMMRainfall data will be obtained from the 0.25° Tropical Rainfall Measuring Mission Project

(TRMM) 3-hourly ‘‘TRMM and others rainfall estimate’’, product 3B42, version 7 (Huffman et al. 2007). The TRMM product combines independent precipitation estimates from multiple microwave and infrared instruments. Data is available equatorward of 50° latitude from 1998 to present. Several studies have confirmed the validity of the TRMM-merged rainfall products in relation to gauge observations and other satellite-based rainfall products in tropical and subtropical regions, including East Africa (Dinku et al. 2007; Asadullah et al. 2008; Monaghan et al. 2012), West Africa (Nicholson et al. 2003), Bangladesh (Islam and Uyeda 2007), tropical Australia (Ebert et al. 2007), Brazil (Franchito et al. 2009), and the U.S. (Wolff et al. 2005).

A.4 Satellite Cloud CoverBecause in situ cloud cover observations are generally not available in many regions, and

even if available can be highly localized, satellite data is often used for cloud cover estimates. Here we plan to use gridded 0.25° latitude x 0.3125° longitude global merged geostationary total cloud percentage data from the Minnis research group at the NASA Langley Research Center (http://cloudsgate2.larc.nasa.gov/index.html). Cloud products are derived using the Visible Infrared Solar-Infrared Split Window Technique (VISST) and Solar-Infrared Infrared Split Window Technique (SIST) (Minnis et al. 2001), and available at hourly intervals near-real time.



Currently this product is available from present back to 2009, so any longer-term simulations can utilize the cloud clover estimates provided by MERRA.

A.5 Container Experiment DataTo provide validation for container water temperature and height estimates from

WHATCH’EM, a series of container experiments were performed at three different locations (Boulder CO, Orizaba MX, and Boca del Rio MX) during boreal summer 2013. At each site, three sets of four different containers (3.8 L “small” black bucket, 19 L “medium” black and white buckets, and 120 L “Large” gray trash can) were set up in different shading regimes (no shade, partial shade, and full shade). Changes in water height were recorded daily and water height re-established to 75% after measurement, with evaporation calculated after accounting for rainfall recorded by a rain gauge. Water temperature sensors were suspended in the middle of the water volume of each container and temperature was automatically recorded every 15 minutes. Air temperature and relative humidity measurements were also recorded at each site. Data are available for approximately four weeks at each site during June-July 2013.

Description of ModelsB.1 WHATCH’EM

Recent work has shown that physics-based approaches toward modeling container water properties are promising for resolving the complexities of container water dynamics (Kearney et al. 2009). WHATCH’EM - The Water Height And Temperature in Container Habitats Energy Model – calculates the temperature and water level of a specified container, based on the energy balance of the water volume. Required variables to be input to WHATCH’EM include air temperature, relative humidity, and rainfall. Optional variables that improve the accuracy of simulations include cloud fraction, near-surface wind speed, soil temperature, and surface (skin) temperature. Container parameters that must be specified include shape (round, rectangular, or tire), dimensions of the main container body and the top opening (diameter or length and width, and height), thickness, reflectivity, thermal conductivity of the container material, and fraction of the container touching the ground surface. Location parameters including latitude, longitude, and elevation are required for solar radiation calculations. Necessary environmental parameters include shade fraction and the amount and time interval of any manual filling of the container.

Once input data and parameters have been specified, the energy balance for water is calculated based on the following equation:

22\*MERGEFORMAT ()

where QWS is heat storage in water (representing the change of temperature of the water), QSW↓ is downward shortwave radiation, QLW↓ is downward longwave radiation, QLW↑ is upwards longwave radiation, QH↑ is upwards/downwards sensible heat transfer, QL↑ is latent heat transfer, QC↓ is conduction between the container bottom and water, and QC→ is conduction between the container side walls and water. The energy balance for the container is:

33\*MERGEFORMAT ()

where QCS is heat storage in the container, QSW→ is sideways inbound shortwave radiation, QLW→ is sideways inbound longwave radiation, QLW← is sideways outbound longwave radiation, QH← is sideways sensible heat transfer, QG↓ is conduction between the ground and the container bottom,



QC↓ is conduction between the container bottom and the water, and QC→ is conduction between the container side walls and water All terms are in units of power (Watts), and the sign convention is that all radiation terms are positive into the water/container and all other terms are positive out of the water/container. Figure 1-2 shows a schematic of the energy balance described by (1) and (2). Each energy balance term is solved in turn for the container and water volume dimensions. The heat storage terms in (1) and (2) are then converted into a temperature change, and evaporation (from the latent heat term), precipitation, and any manual fill are used to calculate the change in water height. Because the calculation of water and container temperature changes involve the terms themselves (through several of the energy balance terms), and form a series of ordinary differential equations, numerical methods are used to simultaneously solve the equation system. In WHATCH’EM, the fourth-order Adams-Moulton (AM) predictor-corrector method is used, which is started with a fourth-order Runge-Kutta (RK) procedure (e.g., Cheney and Kincaid 2008). WHATCH’EM is designed to run in a Linux computing environment. It requires a FORTRAN 90 compiler and there is a driver program written in Perl that requires the Statistics::Descriptive module. Optionally, the NCAR Command Language (NCL) is used for plotting routines.

As described in section A.5, container experiments were recently performed at three different locations. Here we show WHATCH’EM validation results for one of the locations (Boulder CO), for 28 days from 15 June to 12 July 2013. Figure 1-3 shows average daily water temperature range by container and shading for the observations and for WHATCH’EM. The observations show a larger diurnal temperature range for smaller containers, with shading primarily driving differences in daily average temperature. WHATCH’EM has a cold bias overall, especially for no shade conditions, and has an amplified daily temperature cycle for small and medium containers across all shading types. Still, WHATCH’EM captures the variability between shading types and container sizes and colors. Figure 1-4 shows time series of water temperature from observations and WHATCH’EM for the two extreme conditions from the experiment – small black container in no shade (Fig. 1-4a), and large gray container in full shade (Fig. 1-4b). WHATCH’EM represents the day-to-day water temperature variations for both containers, which are largely dependent on cloud cover and wind speed. There is a consistent nighttime cold bias for the small container, up to 5°C.

To evaluate the model simulated water height, Fig. 1-5 shows aggregated evaporation minus precipitation (“E-P”) across the experiment time period for observations and WHATCH’EM. Immediately clear from Fig. 1-5 is the negative bias in E-P for WHATCH’EM across all containers. For the observed containers, the no shade E-P depends primarily on container color (positive relationship between evaporation and water temperature through the heat transfer resistance), however full shade E-P depends on color in an opposite sense (i.e., larger


Figure 1-2. Schematic showing terms of energy balance model introduced in

equations (1) and (2). Terms are described in the text.


evaporation for the translucent white container that receives more diffuse radiation than darker containers). As in the observed partial shade case, WHATCH’EM E-P depends on container size and to a lesser extent container color, for all shading scenarios.

Figure 1-3. Average daily water temperature range (°C) by container and shading for observations (cyan) and WHATCH’EM model (magenta). Black bars represent average temperature over entire time period.



Figure 1-4. Time series of water temperature from observations (cyan) and WHATCH’EM model (magenta) for (a) No shade, small, black container, and (b) Full shade, large, gray container.

Figure 1-5. Evaporation minus precipitation (“E-P”, cm) accumulated over the time period by container and shading for observations (cyan) and WHATCH’EM model (magenta).



The WHATCH’EM validation results, a portion of which are described above, highlight several areas of improvement for the model that will be addressed in this work. The Boulder, Colorado container experiments were performed on a black asphalt surface, which leads to the cold bias for no shade conditions through an urban heat island effect. The surface skin temperature from NLDAS does not represent paved conditions and is therefore too cold for no shade conditions, especially at night. Longwave radiation received to the container sidewalls from the ground, and then conducted to the water volume, is underestimated. For full shade conditions, the cold bias results from longwave radiation emitted from a surrounding metal tractor trailer not represented in the model. The overly active diurnal temperature cycle for small and medium sized containers in partial and full shade results from ground surface temperature values read in from NLDAS, which are too warm because they are not representative of shaded conditions. The desired modification to WHATCH’EM to alleviate surface temperature discrepancies is to explicitly model the surface temperature representative of the area around the container. Similarly, the radiation balance of the shading surface can be modeled to improve representation of longwave power radiated to the container (currently, WHATCH’EM assumes the shading surface temperature is equal to the air temperature). Additionally, to improve longwave flux directly into the container water from the air above, the air temperature at container level will be estimated from the observed 2 m air temperature and modeled surface temperature, fitted with an appropriate mathematical function based on the wind speed and stability. This will also improve the underestimated evaporation from the model, as alleviation of the model nighttime cold bias will lead to higher saturation vapor pressure.

We propose to adapt WHATCH’EM for global applications by using the NASA Earth Science datasets described in Table 1-1 as input (MERRA, GLDAS/NLDAS, TRMM, satellite cloud cover), interpolated to a consistent grid. The framework will be run and validated for boreal summer of 2013 against 1) container observations at three different sites during boreal summer 2013 (described in A.5), and 2) WHATCH’EM simulations driven with in situ observations associated with the experiments described in A.5. Performance of the output from the NASA-based WHATCH’EM framework can be assessed, and biases and deficiencies addressed, with additional container experiments to be performed during boreal summer 2014 if necessary. We will also attempt to validate WHATCH’EM against published studies of water temperature regimes in artificial containers, which include observations from Florida (Cheng et al. 1998), Colombia (Padmanabha et al. 2010) and Australia (Richardson et al. 2012). WHATCH’EM will then be run globally for a variety of scenarios (container characteristics, shading, surface types, etc.). Even though container types, distributions, and uses are different regionally, the estimates provided by WHATCH’EM are general enough to apply globally. Based on data availability, three sets of simulations will be run: 1) Five years (2009-2013) using all datasets, 2) Fifteen years (1999-2013) using MERRA and TRMM, and 3) Thirty-five years (1979-2013) using only MERRA. What we believe to be the best set of input data sources will be run for the time periods of availability, and sensitivity to different sources of input data (i.e., MERRA vs. other datasets) can be assessed for overlap periods. Habitat suitability maps will be produced based on established water temperature ranges for immature Aedes aegypti mosquitoes. A proxy for dengue risk is provided from habitat suitability, but also population estimates, as Ae. aegypti is closely associated with human activity. Population data from the 1 km resolution LandScan dataset produced by Oak Ridge National Laboratory (Dobson et al. 2003) will be used to mask out rural areas with low dengue risk. This work will satisfy project objectives 1-3.



B.2 Skeeter BusterWeather-driven simulation models for Aedes aegypti populations -- such as CIMSiM and

Skeeter Buster -- are strongly influenced by water temperature, which impacts the development times and survival rates of eggs, larvae and pupae (Focks et al. 1993a,b; Cheng et al. 1998; Magori et al. 2009; Ellis et al. 2011). CIMSiM simulates the dynamics of immature Ae. aegypti and water dynamics within user-specified container categories, accounting for shape (circular or rectangular), dimension, presence or absence of lid, fill method (manual or rain), fill frequency (daily, weekly, or monthly), drawdown frequency (daily, weekly, or monthly), if it was located under the edge of a roof or similar device to capture rain water, and if it was in shade or sun (Focks et al. 1993a, Ellis et al. 2011). The original version of CIMSiM used default values for key characteristics of a given container category (Focks et al. 1993a), whereas a more recent version allows the user to input data for the container categories to represent the average characteristics and density of that container category in the focal location (Ellis et al. 2011). The Skeeter Buster model is based on the general characteristics of CIMSiM but operates at the level of individual containers and also incorporates stochastic events, e.g., survival of individuals of Ae. aegypti within a cohort (Magori et al. 2009; Xu et al. 2010). Based on the availability of stochasticity and the ease of code modification, Skeeter Buster will be used in this study.

To test the viability of Skeeter Buster to represent differences in mosquito populations along an elevation and climate gradient in Mexico, simulations were run for boreal summer 2011, and pupae per house results for select locations are shown in Fig. 1-6. Simulations were run for a 25-house neighborhood in each city from 1 May to 15 September, forced by local weather station observations. A distribution of containers was placed in each city based on household sampling results of Lozano-Fuentes et al. (2012). Results from a mosquito collection campaign (Lozano-Fuentes et al. 2012), performed at one time for each city during the summer, are also shown for validation purposes. Even with the regression-based water temperature and height equations used in Skeeter Buster, the model captures the variability along the transect, with pupae per house largest along the eastern lower elevation portion of the transect (Veracruz, Orizaba, and Rio Blanco), decreasing towards higher elevation locations (Acultzingo and Maltrata), and no populations established at high elevation sites (Puebla and Atlixco). Even though there are large deviations in pupae counts across houses in both the observations and Skeeter Buster, the model simulated counts fall near the average observed populations for the study period in each city. Based on the results of Fig. 1-6, we are confident that Skeeter Buster can be a useful tool in describing the seasonality and interannual variability of mosquito populations, which is often missing from direct sampling and statistical models.



Figure 1-6. Time series plots of pupae per house from Skeeter Buster from 1 May to 15 September 2011. Red line is average and red shading represents range from 25 houses. Blue box plot represents average (line),

standard deviation (box), and maximum (whisker) pupae per house from 50 sampled houses around the corresponding date. Plot in lower right shows station locations, elevation, and July/August 2011 mean

temperature for reference.

We will couple WHATCH’EM with Skeeter Buster, so that Skeeter Buster can utilize the physically-based water temperature and height estimates from WHATCH’EM. Mosquito abundance estimates, output from the coupled WHATCH’EM-Skeeter Buster models, will be validated against data from several locations: detailed container distribution and pupal surveys from eastern Mexico (Lozano-Fuentes et al. 2012), Sonora Mexico (from a current NIH-sponsored project with collaborator K. Ernst), and Key West FL (also from an NIH-sponsored project in which K. Ernst is PI), and from published aggregated container and pupal surveys from Chiapas Mexico (Arredondo-Jiménez and Valdez-Delgado 2006) and Puerto Rico (Barrera et al. 2006). The coupled models will be run to provide seasonality and interannual variability of mosquito populations. This work will satisfy project objective 4.

B.3 Climate Model Output for Future Climate ScenariosSensitivity to future climate change of both the habitat suitability maps described in B.1 and

the coupled WHATCH’EM-Skeeter Buster mosquito abundance estimates for selected areas described in B.2 can be assessed using downscaled global climate model projections. Here we plan to use output from the GISS-E2-R global climate model (Schmidt et al. 2006), which uses the GISS ModelE atmospheric model on a 2° x 2.5° latitude-longitude grid with 40 vertical layers, coupled to the Russell ocean model. We will downscale future climate projections for two time periods (2020-2035 and 2060-2075) from GISS-E2-R to MERRA using the linear



correction method of Holland et al. (2010). In this method, downscaled variables are calculated by combining the climatological value from MERRA with an anomaly term from GISS-E2-R. The climatological values are constructed for each three hourly period over the annual cycle. The anomaly values represent deviations of GISS-E2-R from its own climatology (synoptic-scale variability and long-term variability and trends). Three-hourly output is only available for one ensemble member of RCP4.5. We will similarly downscale monthly output of multiple ensemble members of all RCP scenarios, which neglects changes in diurnal cycle but does provide a wider range of future scenarios.

While the simple downscaling method described above only takes into account changes in mean climate and does not represent changes to extreme events, this method is preferable to other statistical downscaling methods (White and Toumi 2013). This work will satisfy project objective 5.

1.3 Perceived Impact to State of KnowledgeThe proposed research will improve Aedes aegypti habitat suitability estimates over current

statistically-based models because water temperature and availability, both key to development and survival of immatures, will be estimated through physical principles by WHATCH’EM. Similarly, mosquito population estimates from dynamic life cycle models like CIMSiM and Skeeter Buster will be improved through use of more realistic and responsive water temperature estimates from WHATCH’EM, compared to the current use of empirical regression equations.

This research will be the best characterization to date of habitat suitability and mosquito population dynamics for several locations across North America and the Caribbean. Through description of the seasonality and interannual variability of mosquito population with the coupled WHATCH’EM-Skeeter Buster models, our knowledge of the climatic factors that modulate Aedes aegypti populations and thereby strongly influence dengue risk will be greatly improved.

Finally, by adapting WHATCH'EM to employ NASA Earth Science datasets, a globally-applicable framework will be in place for assessing habitat suitability and population dynamics for the dengue virus vector mosquito Aedes aegypti. This represents an important achievement, because dengue virus transmission disproportionately occurs in regions where poverty levels are high and therefore in situ weather information is often non-existent or not readily available (e.g., Moore et al. 2012).

1.4 Relevance to Element Programs and Objectives in the NRAThe proposed research supports the Earth Science Research Program’s Applied Science area

(also part of the 2010 Science Plan for NASA’s Science Mission Directorate), which “…develops applications knowledge and understanding of how Earth science can be applied to serve society, increasing the benefits of the nation’s investments in NASA Earth science.” The proposed research will apply Earth science observations (atmospheric and terrestrial) to improve estimates of disease vector habitat suitability and mosquito population dynamics. Also, the habitat suitability maps and mosquito population dynamics estimates produced through this project will be provided to SERVIR, to “…serve as a bridge between the data and knowledge generated by NASA Earth science and the information needs and decision making of Government agencies,…, and others.” The overarching purpose of the Applied Sciences Program is to “discover and demonstrate innovative uses and practical benefits of NASA Earth science research, technology, and observations”, and in the proposed research we use a variety of



NASA Earth science data products to produce information about and proxies of dengue risk. The activities of this project also fit Objective 2.1.7 (“Enable the broad use of Earth system science observations and results in decision-making activities for societal benefits”) in the 2011 NASA Strategic Plan.

In addition to the NASA-specific goals discussed above, the proposed research also contributes to several objectives from the report entitled “National Global Change Research Plan 2012-2021: A Strategic Plan for the U.S. Global Change Research Program”:

Objective 1.1 – Earth system understanding: “Advance fundamental understanding of the physical, chemical, biological, and human components of the Earth system, and the interactions among them, to improve knowledge of the causes and consequences of global change.” Specifically, this research explores the “interaction of climate system processes and other key dimensions of global change, such as ecosystem dynamics” through the connection between climate change and changes to mosquito habitat suitability. Additionally, this research addresses the assertion that “improved integration is needed across multiple aspects and levels of the biological sciences. Processes at the population, species, community, and ecosystem levels are all critical for understanding the causes and consequences of global change.” through integrated modeling of mosquito population dynamics and habitat suitability with climate parameters. Objective 1.3 – Integrated observations: “Advance capabilities to observe the physical, chemical, biological, and human components of the Earth system over multiple space and time scales to gain fundamental scientific understanding and monitor important variations and trends.” This research provides new methods to observe and predict the ecological response to climate, in both improving estimates of container water temperature for habitat suitability, and providing new observationally-based estimates of mosquito population dynamics.

1.5 Work PlanThe work plan for the proposed project is as follows:

Tasks Year 1 Steinhoff Effort (Months)

Modify WHATCH’EM for use with NASA datasets, other improvements 0.75Obtain datasets and run WHATCH’EM for Summer 2013 test period 1.25

Compare simulation results with field data and simulations using in situ data 0.5Manuscript: Demonstration of WHATCH’EM with gridded NASA datasets

Validation 0.5


Construct suitability maps for present day 1.0Construct suitability maps for future climate scenarios 1.25

Provide suitability maps to SERVIR 0.25Manuscript: Suitability maps for present day and future climate scenarios 0.5


Couple WHATCH’EM with Skeeter Buster 1.0



Run and validate coupled models over select areas of North America and the Caribbean 1.5

Manuscript: Modeled Aedes aegypti population dynamics 0.5

1.5.1 Key MilestonesKey Milestone Date

WHATCH’EM Summer 2013 Test Product Sept. 2014Suitability Maps – Present Day June 2015

Suitability Maps – Future Climate Scenarios Oct. 2015WHATCH’EM – Skeeter Buster coupled models June 2016

Assessment of mosquito population dynamics for select areas of North America and the Caribbean

Jan. 2017

1.5.2 Management StructureDr. Daniel Steinhoff of NCAR is the PI and sole investigator of the proposed investigation.

He alone is responsible for the quality and direction of the proposed research and the proper use of all awarded funds. He is solely responsible for all technical and budget issues for the project.

1.5.3 Contributions of Principal InvestigatorDr. Daniel Steinhoff of NCAR will conduct the acquisition of all necessary datasets,

perform all of the analysis, modify and improve the models, and disseminate results at conference proceedings and in peer-reviewed journals.

1.5.4 CollaboratorsDr. Lars Eisen and Dr. Saul Lozano-Fuentes, both of Colorado State University,

Collaborators, will provide information on the bionomics of Aedes aegypti, particularly regarding the use of artificial water containers as sites for egg-laying and development of the immature life states and the impact of water temperature on development times and survival.

Dr. William Crosson, of Science and Technology Institute, Universities Space Research Association, Collaborator, will advise on appropriate NASA remote sensing products to use for this project, including estimates of cloud cover, ground surface characteristics, and any high resolution visible imagery that may be used to characterize container distributions in select areas. Crosson will also assist in adding displays of spatial container suitability maps to the NASA/USAID SERVIR website, which provides environmental decision making tools to developing countries.

Dr. Kacey Ernst, of the University of Arizona, Collaborator, will provide housing characteristics, container distribution data, and pupal surveys for Hermosillo and Nogales Mexico in conjunction with an NIH-funded project on dengue.



2 References and CitationsAdler, R.F., C. Kidd, G. Petty, M. Morissey, and H.M. Goodman, 2003: Intercomparison of

global precipitation products: The Third Precipitation Intercomparison Project (PIP-3). Bull. Amer. Meteor. Soc., 82, 1377-1396.

Arredondo-Jiménez, J.I., and K.M. Valdez-Delgado, 2006: Aedes aegytpi pupal/demographic surveys in southern Mexico: consistency and practicality. Ann. Trop. Med. Parasitology, 100, 17-32.

Asadullah, A., N. McIntyre, and M. Kigobe, 2008: Evaluation of five satellite products for estimation of rainfall over Uganda. Hydrol. Sci. J., 53, 1137–1150.

Bar-Zeev, M., 1958: The effect of temperature on the growth rate and survival of the immature stages of Aedes aegypti (L.). Bull. Entomol. Res., 49, 157-163.

Barrera, R., M. Amador, and G.G. Clark, 2006: Use of the pupal survey technique for measuring Aedes aegypti (Diptera: Culicidae) productivity in Puerto Rico. Am. J. Trop. Med. Hyg., 74, 290-302.

Bartlett-Healy, K., I. Unlu, P. Obenauer, T. Hughes, S. Healy, T. Crepeau, A. Farajollahi, B. Kesavaraju, D. Fonseca, G. Schoeler, R. Gaugler, and D. Strickman, 2012: Larval mosquito habitat utilization and community dynamics of Aedes albopictus and Aedes japonicus (Diptera: Culicidae). J. Med. Entomol., 49, 813- 824.

Bartlett-Healy, K., S.P. Healy, and G.C. Hamilton, 2011: A model to predict evaporation rates in habitats used by container-dwelling mosquitoes. J. Med. Entomol., 48, 712-716.

Beatty, M.E., G.W. Letson, and H.S. Margolis, 2009: Estimating the global burden of dengue.Am. J. Trop. Med. Hyg., 81, 231.

Bhatt, S., and Coauthors, 2013: The global distribution and burden of dengue. Nature, doi:10.1038/nature12060.

Bosilovich, M.G., J. Chen, F.R. Robertson, and R.F. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 2279-2299.

Braks, M.A.H., N.A. Honorio, L.P. Lounibos, R. Lourenco-de-Oliveira, and S.A. Juliano, 2004: Interspecific competition between two invasive species of container mosquitoes, Aedes aegypti and Aedes albopictus (Diptera: Culicidae), in Brazil. Ann. Entomol. Soc. Am., 97, 130-139.

Brunkard, J.M., and Coauthors, 2007: Dengue fever seroprevalance and risk factors, Texas-Mexico border, 2004. Emerg. Infect. Dis., 13, 1477-1483.

Carrington, L.B., S.N. Seifert, N.H. Willits, L. Lambrechts, and T.W. Scott, 2013: Large diurnal temperature fluctuations negatively influence Aedes aegypti (Diptera:Culicidae) life-history traits. J. Med. Entomol., 50, 43-51.

Chang, M.S., J. Hii, P. Buttner, and F. Mansoor, 1997: Changes in abundance and behavior of vector mosquitoes induced by land use during the development of an oil palm plantation in

Sarawak. Trans. Roy. Soc. Trop. Med. Hyg., 91, 382-386.Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface–Hydrology Model with the

Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev., 129, 569-585.



Chen, F., K. Mitchell, J. Schaake, Y.K. Xue, H.L. Pan, V. Koren, Q.Y. Duan, M. Ek, and A. Betts, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251-7268.

Cheney, W., and D. Kincaid, 2008: Numerical Mathematics and Computing. Thomson Brooks/Cole, 763 pp.

Cheng, S., L.S. Kalkstein, D.A. Focks, and A. Nnaji, 1998: New procedures to estimate water temperatures and water depths for application in climate-dengue modeling. J. Med. Entomol., 35, 646-652.

Christophers, S.R., 1960: Aedes aegypti (L.). The Yellow Fever Mosquito. Its Life History, Bionomics and Structure. Cambridge, UK: Cambridge University Press.

Cullather, R.I., and M.G. Bosilovich, 2012: The energy budget of the polar atmosphere in MERRA. J. Climate, 25, 5-24.

Dinku, T., P. Ceccato, E. Grover-Kopec, M. Lemma, S.J. Connor, and C.F. Ropelewski, 2007: Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens., 28, 1503–1526.

Dobson, J.E., E.A. Bright, P.R. Coleman, and B.L. Bhaduri, 2003: LandScan: a global population database for estimating populations at risk. Remotely Sensed Cities, ed.. V. Mesev, London: Taylor & Francis, 267-281.

Ebert, E.E., J.E. Janowiak, C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-88-1-47.

Engelthaler, D.M., T.M. Fink, C.E. Levy, and M.J. Leslie, 1997: The reemergence of Aedes aegypti in Arizona. Emerg. Infect. Dis., 3, 241-242.

Ellis, A.M., A.J. Garcia, D.A. Focks, A.C. Morrison, and T.W. Scott, 2011: Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control. Am. J. Trop. Med. Hyg., 85, 257-264.

Farjana, T., N. Tuno, and Y. Higa, 2012: Effects of temperature and diet on development and interspecies competition in Aedes aegypti and Aedes albopictus. Med. Vet. Entomol., 26, 210-217.

Farnesi, L.C., A.J. Martins, D. Valle, and G.L. Rezende, 2009: Enbryonic development of Aedes aegypti (Diptera: Culicidae): Influence of different constant temperatures. Mem. Inst. Oswaldo Cruz, 104, 124-126.

Focks, D.A., N. Alexander, 2006: A Multi-Country Study on the Methodology for Surveys of Aedes aegypti Pupal Productivity: Findings and Recommendations. Geneva: World Health

Organization.Focks, D.A., D.G. Haile, E. Daniels, and G.A. Mount, 1993a: Dynamic life table model for

Aedes aegypti (Diptera: Culicidae): analysis of the literature and model development. J. Med. Entomol., 30, 1003-1017.

Focks, D.A., D.G. Haile, E. Daniels, and G.A. Mount, 1993b: Dynamic life table model for Aedes aegypti (Diptera: Culicidae): simulation results and validation. J. Med. Entomol., 30, 1018-1028.

Foster, K.R., M.F. Jenkins, and A.C. Toogood, 1998: The Philadelphia yellow fever epidemic of 1793. Sci. Am., 279, 88-93.



Franchito, S.H., V. Brahmananda Rao, A.C. Vasques, C.M.E. Santo, and J.C. Conforte, 2009: Validation of TRMM precipitation radar monthly rainfall estimates over Brazil. J. Geophys.

Res., 114, D02105, doi:10.1029/2007JD009580.Graham, A.S., C.A. Pruszynski, L.J. Hribar, D.J. DeMay, A.N. Tambasco, A.E. Hartley, E.M.

Fussell, S.F. Michael, and S. Isern, 2011: Mosquito-associated dengue virus, Key West, Florida, USA, 2010. Emerg. Infect. Dis., 17, 2074-2075.

Gubler, D.J., 1989: Aedes aegypti and Aedes aegypti-borne disease control in the 1990s: topdown or bottom up. Am. J. Trop. Med. Hyg., 40, 571–578.

Gubler, D.J., 1997: Dengue and dengue hemorrhagic fever: its history and resurgence as a global public health problem. Dengue and Dengue Hemorrhagic Fever, D.J. Gubler and G. Kuno, eds. London: CAB International, 1–21.

Gubler, D.J., 1998: Dengue and dengue hemorrhagic fever. Clin. Microbiol. Rev., 11, 480–496. Gubler, D.J., 2004: Cities spawn epidemic dengue viruses. Nature Medicine, 10, 129–130.Guha-Sapir, D., and D. Schimmer, 2005: Dengue fever: new paradigms for a changing

epidemiology. Emerg. Themes Epidemiol., 2, doi:10.1186/1742-7622-2-1.Guzman, M.G., and G. Kouri, 2002: Dengue: an update. Lancet Infect. Dis., 2, 33-42.Halstead, S.B., 2008: Dengue virus–mosquito interactions. Annu Rev Entomol, 53, 273–291.Hayden, M.H., and Coauthors, 2010: Microclimate and human factors in the divergent ecology

of Aedes aegypti along the Arizona, U.S./Sonora, MX border. EcoHealth, 7, 64-77, doi: 10.1007/s10393-010-0288-z.

Hoeck, P.A., F.B. Ramberg, S.A. Merrill, C. Moll, and H.H. Hagedorn, 2003: Population and parity levels of Aedes aegypti collected in Tucson. J. Vect. Ecol., 28, 65-73.

Holland, G., J. Done, C. Bruyere, C. Cooper, and A. Suzuk, 2010: Model investigations of the effects of climate variability and change on future Gulf of Mexico tropical cyclone activity. Proceedings From OTC Metocean 2010, 20690, OTC.

Hopp, M.J., and J.A. Foley, 2001: Global-scale relationships between climate and the dengue fever vector, Aedes aegypti. Clim. Change, 48, 441-463.

Hotez, P.J., 2008: Neglected infections of poverty in the United States of America. PLoS Neg. Trop. Dis., 2, e256.

Hotez, P.J., M.E. Bottazzi, E. Dumonteil, J.G. Valenzuela, S. Kamhawi, J. Ortega, S. Ponce de Leon Rosales, M.B. Cravioto, and R. Tapia-Conyer, 2012: Texas and Mexico: Sharing a legacy of poverty and neglected tropical diseases. PLoS Negl. Trop. Dis., 6, e1497.

Huffman, G.J., and Coauthors, 2007: The TRMM MultisatellitePrecipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38–55.

Islam, M.N., and H. Uyeda, 2007: Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sens. Env., 108, 264-276.

Juliano, S.A., L.P. Lounibos, and G.F. O'Meara, 2004: A field test for competitive effects of Aedes albopictus on A. aegypti in South Florida: differences between sites of coexistence and exclusion? Oecologia, 139, 583-593.

Kamimura, K., I.T. Matsuse, H. Takahashi, J. Komukai, T. Fukuida, K. Suzuki, M. Artani, Y. Shira, and M. Mogi, 2002: Effect of temperature on the development of Aedes aegypti and Aedes albopictus. Med. Entomol. Zool., 53, 53–58.



Kay, B., and S.N. Vu, 2005: New strategy against Aedes aegypti in Vietnam. Lancet, 365, 613-617.

Kearney, M., W.P. Porter, C. Williams, S. Ritchie, and A.A. Hoffmann, 2009: Integratingbiophysical models and evolutionary theory to predict climatic impacts on species ranges:the dengue mosquito Aedes aegypti in Australia. Funct. Ecol., 23, 528-538.

Koenraadt, C.J.M., and L.C. Harrington, 2008: Flushing effect of rain on container-inhabiting mosquitoes Aedes aegypti and Culex pipiens (Diptera: Culicidae). J. Med.Entomol., 45, 28-35.

Koren, V., J. Schaake, K. Mitchell, Q.Y. Duan, F. Chen, and J.M. Baker, 1999: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res., 104, 19 569-19 585.

Kroeger, A., and M.B. Nathan, 2006: Dengue: setting the global research agenda. Lancet, 368, 2193-2195.

Kumar, S.V., and Coauthors, 2006: Land Information System: An interoperable framework for high-resolution land surface modeling. Environ. Model. Softw., 21, 1402-1415.

Lambrechts, L., K.P. Paaijmans, T. Fansiri, L.B. Carrington, L.D. Kramer, M.B. Thomas, and T.W. Scott, 2011: Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc. Natl. Acad. Sci. U.S.A., 108, 7460-7465.

Lorenz, C., and H. Kunstmann, 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 1397-1418.

Lozano-Fuentes, S., and Coauthors, 2012: The dengue virus mosquito vector Aedes aegypti at high elevation in México. Am. J. Trop. Med. Hyg., 87, 902-909.

Magori, K., M. Legros, M.E. Puente, D.A. Focks, T.W. Scott, A.L. Lloyd, and F. Gould, 2009: Skeeter Buster: a stochastic, spatially explicit modeling tool for studying Aedes aegypti population replacement and population suppression strategies. PLoS Negl. Trop. Dis., 3, e508.

Merrill, S.A., F.B. Ramberg, and H.H. Hagedorn, 2005: Phylogeography and population structure of Aedes aegypti in Arizona. Am. J. Trop. Med. Hyg., 72, 304-310.

Minnis, P., W.L. Smith, Jr., D.F. Young, L. Nguyen, A.D. Rapp, P.W. Heck, S. Sun-Mack, Q.Z. Trepte, Y. Chen, 2001: "A Near-Real Time Method for Deriving Cloud and Radiation Properties from Satellites for Weather and Climate Studies." Proc. AMS 11th Conference on Satellite Meteorology and Oceanography, Madison, WI, Oct. 15-18, 2001.

Mitchell, K.E., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

Mohammed, A., and D.D. Chadee, 2011: Effects of different temperature regimens on the development of Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes. Acta Trop., 119, 38-43.

Monaghan, A.J., K. MacMillan, S.M. Moore, P.S. Mead, M.H. Hayden, and R.J. Eisen, 2012: A regional climatography of West Nile, Uganda, to support human plague modeling. J. Appl. Meteor. Climatol., 51, 1201-1221.

Moore, S.M., A.J. Monaghan, K.S. Griffith, T. Apungu, P.S. Mead, and R.J. Eisen, 2012: Improvement of disease prediction and modeling through the use of meteorological ensembles: Human plague in Uganda. PLoS ONE, 7, doi:10.1371/journal.pone.0044431.



Morrison, A.C., E. Zielinski-Gutierrez, T.W. Scott, and R. Rosenberg, 2008: Defining challenges and proposing solutions for control of the virus vector Aedes aegypti. PLoS Med., 5, e68.

Morrison, A.C., K. Gray, A. Getis, H. Astete, M. Sihuincha, D. Focks, D. Watts, J. D. Stancil, J. G. Olson, P. Blair, and T. W. Scott, 2004: Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol., 41, 1123-1142.

Muturi, E.J., A. Nyakeriga, and M. Blackshear, 2012: Temperature-mediated differential expression of immune and stress-related genes in Aedes aegypti larvae. J. Am. Mosq. Contr. Assoc., 28, 79-83.

Nagao, Y., U. Thavara, P. Chitumsup, A. Tawatsin, C. Chansang, and D. Campbell-Lendrum, 2003: Climatic and social risk factors for Aedes infestation in rural Thailand. Trop. Med. Int. Health, 8, 650-659.

Nicholson, S.E., and Coauthors, 2003: Validation of TRMM and other rainfall estimates with ahigh-density gauge dataset for West Africa. Part II: Validation of TRMM rainfall

products. J. Appl. Meteor., 42, 1355-1368.Padmanabha, H., E. Soto, M. Mosquera, C.C. Lord, and L.P. Lounibos, 2010: Ecological links

between water storage behaviors and Aedes aegypti production: Implications for dengue vector control in variable climates. EcoHealth, 7, 78-90.

Padmanabha, H., C.C. Lord, and L.P. Lounibos, 2011a: Temperature induces trade-offs between development and starvation resistance in Aedes aegypti (L.) larvae. Med. Vet. Entomol., 25, 445-453.

Padmanabha, H., B. Bolker, C.C. Lord, C. Rubio, and L.P. Lounibos, 2011b: Food availability alters the effects of larval temperature on Aedes aegypti growth. J. Med. Entomol., 48, 974-984.

Padmanabha, H., F. Correa, M. Legros, H.F. Nijhout, C.C. Lord, and L.P. Lounibos, 2012: An eco-physiological model of the impact of temperature on Aedes aegypti life history traits. J. Insect Physiol., 58, 1597-1608.

Radke, and Coauthors, 2012: Dengue outbreak in Key West, Florida, USA, 2009. Emerg. Infect. Dis., 18, 135-137.

Ramos, M.M., and Coauthors, 2008: Epidemic dengue and dengue hemorrhagic fever at the Texas-Mexico border: Results of a household-based seroepidemiological survey. Am. J. Trop. Med. Hyg., 78, 364-369.

Reiter, P., and Coauthors, 2003: Texas lifestyle limits transmission of dengue virus. Emerg. Infect. Dis., 9, 86-89.

Renganathan, E., and Coauthors, 2003: Towards sustaining behavioural impact in dengue prevention and control. Dengue Bulletin, 27, 6–12.

Richardson, K., A.A. Hoffmann, P. Johnson, S. Ritchie, and M.R. Kearney, 2011: Thermal sensitivity of Aedes aegypti from Australia: empirical data and prediction of effects on distribution. J. Med. Entomol., 48, 914-923.

Richardson, K., A.A. Hoffmann, P. Johnson, S.R. Ritchie, and M.R. Kearney, 2012: A replicated comparison of breeding-container suitability for the dengue vector Aedes aegypti in tropical and temperate Australia. Austral Ecology, 38, 219-229.

Rienecker, M.M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624-3648.



Rienecker, M.M., and Coauthors, 2008: The GEOS-5 Data Assimilation System - Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, Vol. 27, 92 pp.

Rodell, M., and Coauthors, 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381-394.

Rueda, L.M., K.J. Patel, R.C. Axtell, and R.E. Stinner, 1990: Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J. Med. Entomol., 27, 892-898.

Schmidt, G.A., and Coauthors, 2006: Present day atmospheric simulations using GISS ModelE: Comparison to in-situ, satellite and reanalysis data. J. Climate, 19, 153-192.

Scott, T.W., E. Chow, D. Strickman, P. Kittayapong, R.A. Wirtz, L.H. Lorenz, J.D. Edman, 1993: Blood-feeding patterns of Aedes aegypti (Diptera: Culicidae) collected in a rural Thai

village. J Med Entomol., 30, 922–927.Smith, G.C., D.A. Eliason, C.G. Moore, and E.N. Ihenacho, 1988: Use of elevated temperatures

to kill Aedes albopictus and Aedes aegypti. J. Am. Mosq. Contr. Assoc., 4, 557-558.Steinhoff, D.F., and A.J. Monaghan, 2013: The Water Height and Temperature in Container

Habitats Energy Model (WHATCH’EM). doi:10.5065/D6J67DXP. Tarakidzwa, I., 1997: Evaporation from class-A pans: measurements and modeling. Masters

Thesis. University of Zimbabwe. 109 pp.Tun-Lin, W., and Coauthors, 2009: Reducing costs and operational constraints of dengue vector

control by targeting productive breeding places: a multi-country non-inferiority cluster randomized trial. Trop. Med. Int. Health, 14, 1143-1153.

Tun-Lin, W., T.R. Burkot, and B.H. Kay, 2000: Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med. Vet. Entomol., 14, 31-37.

Walsh, R.K., L. Facchinelli, J.M. Ramsey, J.G. Bond, and F. Gould, 2011: Assessing the impact of density dependence in field populations of Aedes aegypti. J. Vector Ecol., 36, 300-307.

Westaway, E.G., and Blok, J., 1997: Taxonomy and evolutionary relationships of flaviviruses. Dengue and Dengue Hemorrhagic Fever, D.J. Gubler and G. Kuno, eds. London: CAB International, 147-173.

White, R.H., and R. Toumi, 2013: The limitations of bias correcting regional climate model inputs. Geophys. Res. Lett., 40, 2907-2912, doi:10.1002/grl.50612.

Wolff, D.B., D.A. Marks, E. Amitai, D.S. Silberstein, B.L. Fisher, A. Tokay, J. Wang, and J.L. Pippitt, 2005: Ground validation for the Tropical Rainfall Measuring Mission (TRMM). J. Atmos. Oceanic Technol., 22, 365-380.

World Health Organization, 2009: Dengue: guidelines for diagnosis, treatment, prevention and control – New edition. World Health Organization and the Special Programme for Research Training in Tropical Diseases. Geneva: WHO Press.

Xia, Y., M. Ek, J. Sheffield, B. Livneh, M. Huang, H. Wei, S. Feng, L. Luo, J. Meng, and E. Wood, 2013: Validation of Noah-simulated soil temperature in the North American Land Data Assimilation System Phase 2. J. Appl. Meteor. Climatol., 52, 455-471.

Xu, C., M. Legros, F. Gould, and A.L. Lloyd, 2010: Understanding uncertainties in model-based predictions of Aedes aegypti population dynamics. PLoS Negl. Trop. Dis., 4, e830.



Yang, H.M., M.L.G. Macoris, K.C. Galvani, M.T.M. Andrighetti, and D.M.V. Wanderley, 2009: Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue. Epidemiol. Infect., 137, 1188-1202.

Zaitchik, B.F., M. Rodell, and F. Olivera, 2010: Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme. Water Resour. Res., 46, W06507, doi:10.1029/2009WR007811.



3 Biographical Sketch3.1 Principal Investigator

Daniel F. Steinhoff

National Center for Atmospheric Research Phone: 303-497-8466Research Applications Laboratory Fax: 303-497-84013450 Mitchell Lane, Boulder, CO 80301 USA Email: [email protected]

RELEVANT EXPERIENCETwo years experience working with climate effects on the dengue vector mosquito Aedes aegypti for an NSF-funded field and modeling campaign along an elevation transect in eastern Mexico. Primary developer of the Water Height And Temperature in Container Habitats Energy Model (WHATCH’EM). Extensive knowledge and experience with dynamic life cycle mosquito models, atmospheric reanalyses, numerical weather prediction (including land surface models), satellite meteorology, and global climate models.

EDUCATIONPh.D., Atmospheric Sciences, Ohio State University (2011)M.S., Atmospheric Sciences, Ohio State University (2008)B.S., Atmospheric Sciences, University of Wisconsin-Madison (2003)

RESEARCH INTERESTSClimate and human health, Mosquito population modeling (dynamic and statistical), Numerical weather prediction, Data assimilation

PROFESSIONAL EXPERIENCE2011 - Present: Postgraduate Scientist, Research Applications Laboratory, National Center for

Atmospheric Research. 2005 - 2011: Graduate Research Associate, Polar Meteorology Group, Byrd Polar Research

Center, The Ohio State University.

PUBLICATIONSSteinhoff, D. F., A. J. Monaghan, and M. P. Clark, 2013: Projected impact of 21st century

ENSO changes on rainfall in Central America and northwest South America from CMIP5 AOGCMs. Int. J. Climatol., conditionally accepted.

Steinhoff, D. F., D. H. Bromwich, J. C. Speirs, H. A. McGowan, and A. J. Monaghan, 2013: Austral summer foehn winds over the McMurdo Dry Valleys of Antarctica from PolarWRF. Quart. J. Roy. Meteor. Soc., conditionally accepted.

Ballinger, T. J., T. W. Schmidlin, and D. F. Steinhoff, 2013: The Polar Marine climate revisited. J. Climate, 26, 3935-3952, doi:10.1175/JCLI-D-12-00660.1.

Oleson, K. W., A. Monaghan, O. Wilhelmi, M. Barlage, N. Brunsell, J. Feddema, L. Hu, and D. F. Steinhoff, 2013: Interactions between urbanization, heat stress, and climate

change. Climatic Change, conditionally accepted.



Speirs, J. C., H. A. McGowan, D. F. Steinhoff, and D. H. Bromwich, 2013: Regional climatevariability driven by foehn winds in the McMurdo Dry Valleys, Antarctica. Int. J.

Climatol., 33, 945-958, doi:10.1002/joc.3481.Zawar-Reza, P., K. Marwan, I. Soltanzadeh, T. Dallafior, S. Zhong, D. F. Steinhoff, B. Storey,

and C. Carey, 2013: Pseudo-vertical temperature profiles give insight into winter evolution of the atmospheric boundary layer over the McMurdo Dry Valleys of

Antarctica. J. Appl. Meteor. Climatol., 52, 1664-1669.Steinhoff, D. F., D. H. Bromwich, and A. J. Monaghan, 2012: Dynamics of the foehn

mechanism in the McMurdo Dry Valleys Antarctica from Polar WRF. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2038.

Lozano-Fuentes, S., M. H. Hayden, C. Welsh-Rodriguez, C. Ochoa-Martinez, B. Tapia-Santos, K. C. Kobylinski, C. K. Uejio, E. Zielinski-Gutierrez, L. Delle Monache, A. J.

Monaghan, D. F. Steinhoff, and L. Eisen, 2012: The Dengue Virus Mosquito Vector Aedes aegypti at High Elevation in México. American Journal of Tropical Medicine and Hygiene, 87(5), 902-909, doi:10.4269/ajtmh.2012.12-0244.Lozano-Fuentes, S., C. Welsh-Rodriguez, M. H. Hayden, B. Tapia-Santos, C. Ochoa-Martinez,

K. C. Kobylinski, C. K. Uejio, E. Zielinski-Gutierrez, L. Delle Monache, A. J. Monaghan, D. F. Steinhoff, and L. Eisen, 2012: Aedes (Ochlerotatus) epactius Dyar & Knab along an elevation and climate gradient in Veracruz and Puebla States, México. J. Medical Entomology, 49(6), 1244-1253, doi:10.1603/ME12067.

Bromwich, D. H., D. F. Steinhoff, I. Simmonds, K. Keay, and R. L. Fogt, 2011: Climatological aspects of cyclogenesis near Adélie Land Antarctica. Tellus, 63A, 921-938,

doi:10.1111/j.1600-0870.2011.00537.x.Speirs, J. C., D. F. Steinhoff, H. A. McGowan, D. H. Bromwich, and A. J. Monaghan, 2010:

Foehn winds in the McMurdo Dry Valleys, Antarctica: The origin of extreme warming events. J. Climate, 23, 3577-3598, doi:10.1175/2010JCLI3382.1.

Steinhoff, D. F., S. Chaudhuri, and D. H. Bromwich, 2009: A new perspective on the Ross Ice Shelf Air Stream. Mon. Wea. Rev., 137, 4030-4046, doi:10.1175/2009MWR2880.1.

Steinhoff, D. F., D. H. Bromwich, M. Lambertson, S. L. Knuth, and M. A. Lazzara, 2008: A dynamical investigation of the May 2004 McMurdo Antarctica severe wind event

using AMPS. Mon. Wea. Rev., 136, 7-26, doi:10.1175/2007MWR1999.1.

COMMUNITY ACTIVITIES Representative for Atmospheric Sciences to the Council of Graduate Students (CGS),

The Ohio State University, 2006 – 2007. 43 presentations to school field trips (K-12 and college) to Byrd Polar Research Center,

January 2008 – March 2011. Mentor to Ruth Burrows, Upper Arlington High School, Upper Arlington OH, science

fair project, meetings every 1-2 weeks, November 2006 – February 2007. Mentor to Saptarshi Chaudhuri, Columbus Alternative High School, Columbus OH,

summer internship, approximately 40 hours per week, June – August 2008. This work resulted in a manuscript in Monthly Weather Review. Saptarshi completed undergraduate studies at Caltech and is now in graduate studies at Stanford.

Mentor to Gabriela D. Talavera-Santiago, UCAR/NCAR Spark Pre-College Internship Program, June – August 2013.



4 Current and Pending Support4.1 Current AwardsDaniel Steinhoff, PIName of Principal

Investigator on Award

Award/Project TitleProgram Name/Sponsoring

Agency/Point of Contact telephone and email

Period of Performance/Total

Budget

Commitment (Person-

Months per Year)

Andrew Monaghan The Vector Mosquito Aedes aegypti at the Margins: Sensitivity of a Coupled Natural and Human System to Climate Change

NSFSarah Ruth Ph: (703) [email protected]

10/1/2010-9/30/2013$1,235,153

12.0

Andrew Monaghan Disease Vector Mapping via Environmental/Climatological/Sociological factors

STAR, LLC Paul BieringerPh: (303) 640-5592 [email protected]

6/17/2013-6/16/2014$147,116

3.0

4.2 Pending AwardsDaniel Steinhoff, PIName of Principal

Investigator on Award

Award/Project TitleProgram Name/Sponsoring

Agency/Point of Contact telephone and email

Period of Performance/Total

Budget

Commitment (Person-Months

per Year)Andrew Monaghan An Operationally

relevant, Data-Driven Predictive Model That Estimates Timing and Spatial extent of Exotic Vector-Borne Disease Transmission Risk to Humans in the United States

USDA ARS CTR Medical, Agricultural, Vet. EntomologyKenneth Linthicum Ph: (352) 374-5700 [email protected]

1/1/2014-12/31/2018$346,000

2.0

David Yates Regional Climate Modeling for the Arabian Gulf Region – Future Scenarios and Capacity Building

Climate Change Research Group, LLC Bill DoughteryPh: (508) [email protected]

7/1/2013-6/30/2014$152,995

3.0



5 Budget Justification: Narrative and Details5.1 Budget Narrative5.1.1 Personnel and Work EffortTitle Employee Year 1 Year 2 Year 3Associate Scientist III, NCAR PI Dr. Daniel Steinhoff 25% 25% 25%

NCAR PI Daniel Steinhoff will work approximately 25% in each year of the project and will have primary responsibility for all model development, production of water container suitability maps, and dissemination of results in conference proceedings, journal publications, and possibly NASA SERVIR.

5.1.2 Facilities and EquipmentNCAR investigators have access to all of the computational facilities required for this project including: multiple laptops and desktop computers with appropriate graphical, word processing, statistical, and data processing software; peripherals; printers; and network connections within the Research Applications Laboratory (RAL). The National Center for Atmospheric Research (NCAR) and RAL have site licenses to Portland Group compilers.

5.2 Budget Details

A. Senior / Key Person

An Associate Scientist III will serve as the NCAR Principal Investigator and will charge approximately 25% in each year of the project with a salary range between $78,000.00 – $87,994.94. This labor will include input data acquisition and processing, continued development of the WHATCH’EM container model, coupling of WHATCH’EM with the mosquito population model Skeeter Buster, analysis of mosquito suitability for present day and future climate scenarios, and dissemination of final results.

A 4% annual salary increase has been included.

Fringe Benefits:

The salary budget includes a full time employee benefit rate of 53.8% for non-work time of vacation, sick leave, holidays and other paid leave, as well as standard staff benefits. Worked hours are based on 86% of 2080 hours in a year.

B. Other Personnel: None

C. Equipment: None

D. Travel:



Domestic Travel: A total of $15,980 is budgeted for domestic travel over the life of the project. This includes travel to two conferences: AGU in San Francisco and the International Society for Photogrammetry and Remote Sensing (ISPRS) conference, which rotates locations but priced for Washington DC. Each would be 5 days/nights for 1 person.

All costs (based on NCAR travel rates) include airfare, lodging, car rental, IRS-approved per diem rates, and registration costs and are escalated by 4% per year.

Destination Airfare Hotel Car Per DiemConf. Reg

& Misc Total Trip

Cost

Yr 1 - Travel - Trip OneDenver, CO to Washington, DC 438.80$ 1,368.00$ 390.50$ 649.95$ 2,847.25$ 1 Traveler .5 DaysYr 1 - Travel - Trip TwoDenver, CO to San Francisco, CA 301.80$ 930.00$ 390.50$ 649.95$ 2,272.25$ 1 Traveler5 DaysTotal for Yr 1 Travel 5,119.50$

Yr 2 - Travel - Trip OneDenver, CO to Washington, DC 438.80$ 1,368.00$ 390.50$ 649.95$ 2,847.25$ 1 Traveler5 DaysYr 2 - Travel - Trip TwoDenver, CO to San Francisco, CA 301.80$ 930.00$ 390.50$ 649.95$ 2,272.25$ 1 Traveler5 DaysSub-total for Yr 2 Travel 5,119.50$ Total with 4% Escalation on Yr 2 Travel 5324.28Yr 3 - Travel Denver, CO to Washington, DC 438.80$ 1,368.00$ 390.50$ 649.95$ 2,847.25$ 1 Traveler5 DaysYr 3 - Travel Denver, CO to San Francisco, CA 301.80$ 930.00$ 390.50$ 649.95$ 2,272.25$ 1 Traveler 5 DaysSub-total for Yr 3 Travel 5,119.50$ Total with 4% Escalation on Yr 3 Travel 5,537.25$

E. Participant Support Costs: None.

F. Other Direct Costs:

Materials and Supplies: $1,000 per year has been requested for Materials and Supplies and will include purchase of miscellaneous office supplies. The additional $3.5K in year 1 would be for a laptop computer



(Apple Macbook Pro 13 in. and accessories, $2250) and a 20TB QNAP RAID disk storage device ($1250).

Publication Costs: Publication costs/page charges of $3,500 per year (based on previous costs) are requested for one journal article per year.

Consultant Services: None

Computer Services: Scientific, computing and networking support costs have been allocated to this project through the Computer Service Center (CSC), in accordance with OMB circulars and NCAR management policy. The CSC rate for 2014 is $7.20 per labor hour.

Subawards: None

Other: None

F. Direct Costs: $132,055

H. Indirect Costs: $71,972 is requested for indirect costs.Indirect Costs are applied to all modified total direct costs (MTDC). Excluded from MTDC are items of equipment costing $5,000 or more, and individual subcontract amounts in excess of at least $25,000 per fiscal year. The provisional FY14 rate for Indirect Costs is 58.8%. Cognizant Agency: National Science Foundation (NSF).

I. Total Direct and Indirect Costs: $204,027 is requested for direct and indirect costs.

J. Fee: $6,121

A 3% UCAR management fee has also been included. The UCAR management fee is a fixed fee, calculated as a percent of proposed MTDC and NCAR applied Indirect Costs.

K. Budget Total: $210,148




Prop

osal

#20

13-00

60

UCAR

Enti

ty:NC

ARPe

riod o

f Perf

orman

ce:

03-01

-2014

- 02

-28-20

17Pr

incipl

e Inv

estig

ator

DANIE

LST

EINHO

FF

Year

1Ye

ar 2

Year

3NA

SANA

SANA

SA

Regu

lar Sa

laries

ASSO

C SC

IENTIS

T III (

TBD)

0.25

0.25

0.25

18,62

519

,371

20,14

558

,141

18,62

519

,371

20,14

558

,141

Re

gular

Ben

efits

@10

,020

10,42

210

,838

31,28

010

,020

10,42

210

,838

31,28

0

28,64

529

,793

30,98

389

,421

Comp

uter /

Lapto

p2,2

500

02,2

50Pu

blica

tion /

Pag

e Cha

rges

3,500

3,500

3,500

10,50

0Ge

neral

Mate

rials

1,000

1,000

1,000

3,000

Comp

uter S

uppli

es &

Peri

phera

ls1,2

500

01,2

508,0

004,5

004,5

0017

,000

Dome

stic -

To at

tend A

GU C

onfer

ence

2,272

2,363

2,457

7,092

Dome

stic -

To at

tend I

SPRS

Con

feren

ce2,8

472,9

613,0

808,8

885,1

195,3

245,5

3715

,980

41,76

439

,617

41,02

012

2,401

NCAR

Indir

ect C

ost R

ate (M

TDC)

24,55

723

,295

24,12

071

,972

24,55

723

,295

24,12

071

,972

Comp

uting

Servi

ce Ce

nter

Comp

uting

Serv

ice C

enter

3,218

3,218

3,218

9,654

3,218

3,218

3,218

9,654

69,53

966

,130

68,35

820

4,027

Mana

geme

nt Fe

eUC

AR M

anag

emen

t Fee

2,086

1,984

2,051

6,121

71,62

568

,114

70,40

921

0,148

UCAR

Prop

osal

Budg

et De

tail

Prop

osal

Title:

Glob

al mo

delin

g of th

e clim

atic s

uitab

ility o

f artif

icial

water

conta

iners

for br

eedin

g the

de

ngue

vecto

r mos

quito

Aed

es ae

gypti

using

remo

tely s

ense

d data

for p

resen

t day

and

clima

te ch

ange

appli

catio

ns

Unit /

Rate

Eff

ort

Year

1Eff

ort

Year

2Eff

ort

Year

3Cu

mulat

ive Gr

and

Total

Salar

iesFT

ESu

btotal

Salar

iesFri

nge B

enefi

ts53

.80 %

Subto

tal Fr

inge B

enefi

ts

Total

Salar

ies an

d Ben

efits

Mater

ials a

nd Su

pplie

s

Subto

tal M

ateria

ls an

d Sup

plies

Trave

l

Subto

tal Tr

avel

Modif

ied To

tal Di

rect C

osts

(MTD

C)Ind

irect

Costs

58.80

%To

tal In

direc

t Cos

ts

3.00 %

Total

Fund

ing To

UCAR

MTDC

Costs

that

Includ

e Ind

irect

Costs

$7.20

/ hr

Subto

tal M

TDC C

osts

that In

clude

Indir

ect C

osts

Total

MTD

C + Ap

plied

Indir

ect C

osts


UCAR Standard Information



Rate Letter FY14


Scientific/Technical/Management - NCAR Research ... · Web viewAnother important factor to Ae....

Documents

Transcript of Scientific/Technical/Management - NCAR Research ... · Web viewAnother important factor to Ae....