The Role of Surface Forcing on Significant Precipitation

Events in a Mesoscale Model

 

Richard. M. Hodur and Bogumil Jakubiak

 Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Warsaw, Poland

12th EMS Annual Meeting & 9th European Conference on Applied Climatology (ECAC) | 10 – 14 September 2012 | Łódź, Poland

•Background/Objective/Approach•Model Description•Land-Surface Model (LSM) Description•Design of Experiments•Case Studies•Results•Conclusions•Future Plans

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelOutline

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelBackground/Objective/Approach

BackgroundThe lower boundary conditions are critical for surface-forced mesoscale events, and proper treatment of them can lead to forecast improvements not attainable in lower-resolution (i.e., global) models.

ObjectiveThe objective of this project is to integrate a land-surface model (LSM) into a high-resolution mesoscale model and evaluate the impact of the LSM on forecasts of heavy precipitation events over Poland.

Approach• Use COAMPS as the mesoscale model• Integrate the NOAH LSM into COAMPS®• Run COAMPS and COAMPS w/LSM on heavy precipitation events• Validate the results of the forecasts

COAMPS: Coupled Ocean/Atmosphere Mesoscale Prediction SystemCOAMPS is a registered trademark of the Naval Research Laboratory

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelModel Description: COAMPS

•Analysis:• Atmosphere: MVOI Analyses of Wind, Temperature, and Heights/Pressure• Ocean: 2D OI of SST (NCODA)

•Model:• Numerics: Nonhydrostatic, Scheme C, Sigma-z, Flexible Lateral BCs• Parameterizations: PBL, Convection, Explicit Moist Physics, Radiation, Surface Layer• Land Surface: Slab Model or NOAH LSM

•Features:• Globally Relocatable (5 Map Projections)• User-Defined Parameters: Grid Resolutions, Dimensions, and # of Parent/Nested Grids• Data Assimilation

MVOI: Multi-Variate Optimum InterpolationNCODA: NRL Coupled Ocean Data Assimilation

Coupling to ocean and wave models not used in this study

NOAH Land-Surface Model (LSM)National Centers for Environmental Prediction (NCEP)

Oregon State University (Department of Atmospheric Sciences)Air Force (AFWA and AFRL)

Hydrologic Research Laboratory (NWS)

dS = P – R – EdS = Change in soil moisture content and snowpackP = PrecipitationR = Runoff E = Evapotranspiration (EDIR + EC + ETT + ESNOW)

EDIR: Evaporation from top soilEC: Evaporation from canopy intercept and dewETT : Transpiration through canopy via root uptakeESNOW: Sublimation from snowpack

NOAH LSM Surface Water Budget

Evapotranspiration is a function of soil moisture, vegetation type, rooting depth/density, fraction of green vegetation, and canopy resistance.

NOAH LSM Surface Energy Budget

Rnet = SH + LH + GH + SPGHRnet = Net radiation (SW - SW + LW - LW)SH = Sensible heat fluxLH = Latent heat flux (surface evaporation)GH = Ground heat flux (subsurface heat flux)SPGH = Snow phase-change heat flux (heat sink of melting snow) LW = ε σ (Tskin)4

ε in the NOAH LSM is always 1.0 except for presence of snowpack

Present: 1.0*(1 - SnowFraction) + 0.95*SnowFractionFuture:

• look-up tables based on vegetation and soil type, and/or•physical/empirical model, and/or•satellite retrieval

Topography,Soils

Land Cover, Vegetation Properties

Meteorology

Snow Soil MoistureTemperature

Land Surface Models (Noah,Mosaic,CLM,VIC,SiB,CLSM,..)

Data Assimilation Modules(DI,EKF,EnKF)

Soil Moisture &

Temperature

Evaporation

Runoff

SnowpackProperties

Input OutputPhysics

Weather/Climate

Water Resources

HomelandSecurity

Military Ops

Hazards

Applications

Christa Peters-Lidard et. al., NASA

NASA Land Information System (LIS)

A high-performance uncoupled infrastructure forGlobal Land Data Assimilation System

COAMPS LSM uses the output from the NASA LIS as input to the NOAH LSM

• Double-Nested Grid:• 9 km (255x255)• 3 km (310x310)

• 40 Levels• Forecasts:

• Run to 24 hours• Explicit treatment of

convection on both grids• GFS used for IC (cold starts)

and Lateral Boundary Conditions

Impact of a Land Surface Model (LSM) in a Mesoscale Model on the Prediction of Heavy

Precipitation EventsGrid/Forecast Details

9 km

3 km

NOAH Landuse Categories and Distribution over the COAMPS Grid

NOAH Landuse Categories 1: urban and built-up land 2: dryland cropland and pasture 3: irrigated cropland and pasture 4: mixed dryland/irrigated cropland and pasture 5: cropland/grassland mosaic 6: cropland/woodland mosaic 7: grassland 8: shrubland 9: mixed shrubland/grassland10: savanna11: deciduous broadleaf forest12: deciduous needleleaf forest13: evergreen broadleaf forest14: evergreen needleleaf forest15: mixed forest16: water bodies17: herbaceous wetland18: wooded wetland19: barren or sparsely vegetated20: herbaceous tundra21: wooded tundra22: mixed tundra23: bare ground tundra24: snow or ice

Each grid square uses the landuse category that best represents the conditions in that grid square

Distribution of the NOAH Landuse Categories for the 3 km grid

Used to define surface albedo, roughness

Warsaw(Category 1)

Soil Types for the LSM16 Soil Types

1: Sand 2: Loamy sand 3: Sandy loam 4: Silt loam 5: Silt 6: Loam 7: Sandy clay loam 8: Silty clay loam 9: Clay loam10: Sandy clay11: Silty clay12: Clay13: Organic materials14: Water15: Bedrock16: Other Distribution of Soil Type on the 3 km Grid

Used to define thermal conductivity, soil porosity, etc.

There may be inconsistencies between the land use category and the soil type. This will be addressed in constructing a full land surface data assimilation

scheme.

Warsaw

Greenness Fraction for the LSM

The Greenness Fraction is a

function of plant type and season, and is a measure of how green the

vegetation is (related to

photosynthesis).

Distribution of the Greenness Fraction on the 3 km Grid

Ground Wetness for the NOLSM and the LSM on the 3 km Grid

Significant NO-LSM / LSM differences in the ground wetnessNOLSM LSM

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelDesign of Experiments: Test Cases

May 2010• 0000 GMT 5 May 2010• 0000 GMT 10 May 2010• 0000 GMT 13 May 2010• 0000 GMT 18 May 2010• 0000 GMT 20 May 2010• 0000 GMT 24 May 2010

August 2010• 0000 GMT 3 August 2010• 0000 GMT 6 August 2010• 0000 GMT 9 August 2010• 0000 GMT 12 August 2010• 0000 GMT 13 August 2010• 0000 GMT 15 August 2010• 0000 GMT 16 August 2010• 0000 GMT 22 August 2010

Experiment NamesNOLSM: Use original slab modelLSM: Use NOAH LSM with LIS

• First run for each case is a cold start, 6 h warm start for two subsequent runs• GFS used for IC (cold starts only) and LBC• MVOI analysis on all 3 grids

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelData Assimilation

Observations

6 h Forecast/First-Guess

MVOI Analysis

GFSFirst-Guess

Observations

6 h Forecast/First-Guess

MVOI Analysis

Observations

24 hour Forecast

MVOI Analysis

Verification• Objective (Raobs, surface observations)• Subjective (Model vs. observed precipitation)• NOLSM (control) vs. LSM (beta)

Use of the LSM appears to help reducing a cold,

low-level temperature bias

Obs Level Parameter Error-Type WPL Err Ctl Err Bta Dif ---- --- -- ---------- ---------- --- ------- ------- ----- Raob 250 mb Wind (m/s) Vector RMS 0 6.89 6.95 -0.05 Raob 250 mb Height (m) STD DEV 0 13.92 13.66 0.26 Raob 250 mb Height (m) Mean Error 0 -0.99 3.52 -0.77 Raob 250 mb Temp (C) STD DEV 0 1.32 1.25 0.07 Raob 250 mb Temp (C) Mean Error 0 0.99 0.95 0.06 Raob 500 mb Height (m) STD DEV 0 7.44 7.45 -0.01 Raob 500 mb Height (m) Mean Error 0 -5.22 -2.73 1.82 Raob 500 mb Temp (C) STD DEV 0 0.80 0.82 -0.02 Raob 500 mb Temp (C) Mean Error 0 -0.06 0.08 -0.03 Raob 500 mb T_d (C) STD DEV 0 5.46 5.78 -0.34 Raob 500 mb T_d (C) Mean Error 0 -0.27 -0.46 -0.09 Raob 850 mb Wind (m/s) Vector RMS 0 4.32 4.42 -0.10 Raob 850 mb Height (m) STD DEV 0 5.75 6.04 -0.28 Raob 850 mb Height (m) Mean Error 0 -3.37 -2.42 -0.87 Raob 850 mb Temp (C) STD DEV 0 1.20 1.27 -0.08 Raob 850 mb Temp (C) Mean Error 0 -0.29 -0.19 0.08 Raob 850 mb T_d (C) STD DEV 0 2.09 2.15 -0.05 Raob 850 mb T_d (C) Mean Error 0 -0.12 0.17 -0.02 Sfc 10 m Wind (m/s) Vector RMS 0 2.65 2.65 -0.08 Sfc 2 m Temp (C) STD DEV 0 1.65 1.75 -0.10 Sfc 2 m Temp (C) Mean Error 0 -0.19 0.42 -0.18 Sfc 2 m T_d (C) STD DEV 0 1.57 1.82 -0.25 Sfc 2 m T_d (C) Mean Error 0 0.05 0.19 -0.07

Objective Scoring Results*24 hour forecasts for all 14 cases

Differences between the NOLSM and the LSM runs are not statistically significant *Jason Nachamkin, NRL

WPLW: Win (+1)P: Push ( 0)L: Loss (-1)

Subjective Evaluation of Test CasesLSM forecasts on highlighted dates produced superior forecasts

in terms of movement of precipitation events

May 2010• 0000 GMT 5 May 2010• 0000 GMT 10 May 2010• 0000 GMT 13 May 2010• 0000 GMT 18 May 2010• 0000 GMT 20 May 2010• 0000 GMT 24 May 2010

August 2010• 0000 GMT 3 August 2010• 0000 GMT 6 August 2010• 0000 GMT 9 August 2010• 0000 GMT 12 August 2010• 0000 GMT 13 August 2010• 0000 GMT 15 August 2010• 0000 GMT 16 August 2010• 0000 GMT 22 August 2010

Case Study #1Initial Conditions: 0000 UTC 16 August 2010

10 m winds (kts) at 18 and 21 hours

Analysis @ 18 hNOLSM @ 18 h

NOLSM @ 21 h

LSM @ 18 h

LSM @ 21 hConvergence line in LSM run is closer to

the observed line than in the NOLSM run; also, LSM run has

better defined frontal structure.

Similar results in 3 other cases.

Case Study #1Initial Conditions: 0000 UTC 16 August 2010

COAMPS radar reflectivity represents the maximum found in a vertical column

NOLSM @ 18 h

Observed @ 21 hNOLSM @ 21 h LSM @ 21 h

LSM @ 18 hObserved @ 18 h

The location and movement of the precipitation are forecast better in the LSM forecast

Case Study #1Initial Conditions: 0000 UTC 16 August 2010

10 m Air Temperature (K)

NOLSM @ 21 h LSM @ 21 h10 m temperatures

exhibit a larger range and better-

defined frontal structure in the

LSM run

Time-history of 10 m temperatures at

black dot show passage of stronger front earlier in the

LSM run

In general, the 10 m air temperatures are warmer in the LSM run, consistent with the LSM reducing the COAMPS low-level temperature bias.

Case Study #2Initial Conditions: 0000 UTC 22 August 2010

COAMPS radar reflectivity represents the maximum found in a vertical column

VerificationNOLSM @ 22 h LSM @ 22 h

The LSM run maintains the precipitation in NE Poland better, and while slow, the LSM does a better job in the development of precipitation in

SW Poland.

Case Study #3Initial Conditions: 0000 UTC 07 May 20101000 mb 12 h Forecast Temperature Field

The LSM run consistently predicts slightly warmer temperatures in the low levels

NOLSM @ 22 h LSM @ 22 h

• Conclusions• 14 separate precipitation events over Poland in 2010 were simulated using

COAMPS with (a) a slab model and (b) the NOAH LSM• Objective verification methods showed no significant differences in the

forecast skill between these runs; agrees with results from similar studies• More significant differences may be found for longer data assimilation

experiments that include many different forecast scenarios• The location of precipitation areas, the translation speed, and the

developmental time for several of the precipitation systems was simulated better using the LSM; other cases showed no substantial differences

• Use of the LSM reduced a known COAMPS low-level temperature bias• Results are dependent on the time of the year and the area

• Future Plans• Conduct further studies using NOLSM, LSM• Build a mesoscale land data assimilation infrastructure around COAMPS,

similar to NASA LIS*

The Role of Surface Forcing on Significant Precipitation Events in a

Mesoscale ModelConclusions/Future Plans

The Role of Surface Forcing on Significant Precipitation

Events in a Mesoscale Model

 

Richard. M. Hodur and Bogumil Jakubiak

 Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Warsaw, Poland

12th EMS Annual Meeting & 9th European Conference on Applied Climatology (ECAC) | 10 – 14 September 2012 | Łódź, Poland