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• ESE Science Review[Meeting on Weather Forecasting]

• Eric A. Smith; NASA/Goddard Space Flight Center, Greenbelt, MD 20771[301-286-5770; 301-286-1626; easmith@pop900.gsfc.nasa.gov]

• July 20, 2001; NASA Headquarters, Washington, DC

GPM

A Limited Perspective on Weather ForecastScience Problems Confronting GPM

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TRMM 1-day coverage

SSM/I Era

TRMM Era

EOS Era

GPM Era

3

Projected Satellite Data Streams for GPM Erafrom Passive Microwave Radiometers & Precipitation Radars

[at left are either actual (bold) or orthodox (paren) nodal crossing times (DN or AN) or non-sun-synch labels ]

CY 99-18 99 00 11 12 13 14 15 16 17 1803 08 09 1001 02 0704 05 06

AMSR-E CMR-1

F20

NPOESS C1

F18 SSM/I SSMIS CMIS

SSM/I SSMIS CMIS MSU AMSU-A

AMSR AMSR-FO

GPM-1 (e.g., N-GPM)

GPM Core (65∞ inc)

ADEOS II

PR/TMI DPR/ATMI

TRMM (35∞ inc)

DMSP F13DMSP F16

NPOESS C3

DMSP F15DMSP F17 F19

NPOESS LITE-CMIS

AQUA

GPM-3 (e.g., E-GPM)

GPM-2 (e.g., I-GPM)

GCOM-B1

MEGHA-TROPIQUES (22∞ inc)MADRAS

GPM-5 (partner needed)

GPM-4 (partner needed)

CMR-2

CMR-3

CMR -4

CMR-5

FY-3TBD

0530DN0530DN

0130DN

NSS

NSS

1030DN

TBD

NOAA-MNOAA-K CMIS MSU AMSU-A

0730DN

NOAA-L NOAA-N

Potential Gap

KEY carries preferred PMW frequencies carries alternate PMW frequenciesGPM-1,-2,-3,-4,-5 are dedicated GPM drone

satellites (e.g., N-GPM, I-GPM, E-GPM)

NOAA-J

(1430AN)

(1730AN)

(2030AN)

(2330AN)

(1030DN)

(0230DN)

NPOESS C2

NOAA-N’

Continuous Geosynchronous Satellite Coverage by GOES E/W, METEOSAT/MSG, & GMS

(0830DN)

(0530DN)

NPP-ATMS

Replacement Era

Replacement Era

Replacement Era

Replacement Era

Replacement Era

Replacement Era

0915DN0830DN

4

GPM Mission Reference Concept

GPM Advanced Study Formulation

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GPM Science Agenda

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GPM Constellation Orbit Optimization

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GPM Mission is Being Formulatedwithin Context of GWEC

with Main Science Objectives Focusing On:_____________________________________________________________________________________________

Improving flood hazard & basin-scale hydrological predictions -- through more frequent sampling and full-earth coverage of precipitation measurements

Improving climate prediction -- through better understanding of water cycling and accompanying accelerations -- decelerations of atmospheric and surface branches of water cycle

Improving weather forecasting -- through better methods of rainfall data assimilation and more accurate & precise measurements of instantaneous rainrates

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GPM Era Coverage GPM Era Coverage with 3 Inclined

GPM Core, DMSP-F18, DMSP-F19, GCOM-B1, Megha-Tropiques,& Three 600-km DronesDrones @ 34∞, 84∞, 90∞

3-hour Ground Trace

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Tropical CyclonePrediction

Conundrum

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TRMM Impact on Mesoscale Simulation of Super Typhoon Paka

PAKA (8.9∞N, 161.8∞E)[13 Dec-1997 / 0911 UTC]

SSM/I 85 GHz TB

GEOS with TRMM RR & TPW + Bogus Vortex(adjoint-based 4-D VAR)[13 Dec-1997/ 0900 UTC]

RR(mm/3hr); LP; 850 hPa Wind

GEOS without TRMM [13 Dec-1997 / 0900 UTC]

RR(mm/3hr); SLP; 850 hPa Wind

GEOS with TRMM RR & TPW[13 Dec-1997 / 0900 UTC]

RR(mm/3hr); SLP; 850 hPa Wind

[Pu & Tao, 2001: GSFC]

33 hr forecasts using PSU/NCAR MM5 model at 5-km horizontal resolution testing different initial conditions for time 12 Dec-1997 / 0000 UTC

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Retrieval of RainrateVertical Structure

Conundrum

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Data Assimilation Experiments Based on Retrieved SSM/I &

TRMM Rainrates

Have Not Been Particularly Sensitive to Intensity of

Rainrates

Nor Have Made Use of Vertical Profile of

Rainrate or Latent Heating

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Cloud-PrecipitationContinuumConundrum

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Impact of Rainfall Assimilation on GEOS Analysis

Assimilationwith

TMI+SSM/Irainfall & TPW

Precipitation

verified against

GPCP____________________

TPW

verified against

Wentz____________________

OLR

verified against

CERES/TRMM____________________

IR Cld Forcing

verified against

CERES/TRMM

Assimilation of satellite-based rainfall data improves clouds & TOA radiation,Assimilation of satellite-based rainfall data improves clouds & TOA radiation,

plus reduces state-dependent systematic errors in GEOS analysisplus reduces state-dependent systematic errors in GEOS analysis

OLR Error Std Dev OLR Error Std Dev

verified against CERESverified against CERES

for 1-, 5-, & 30-dayfor 1-, 5-, & 30-day

Averaging PeriodsAveraging Periods

Control Run

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Physical InitializationUnder

Complex Convective Parameterization Scheme

Conundrum

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Reverse Schemes for Convective Parameterizations

Carrying Updrafts & Downdrafts

Do Not Yield Unique Solutionfor Adjusting (nudging)

Water Vapor Field

Once Model-ObservationSurface Rainfall Departures

Are Determined

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Control variables at time t0

Control variablesat time t

Surface rainrate

Forecast Model

(PHYSICS)

Interpolation at observation

location

MOIST PHYSICS(convection+

grid scalecondensation)

Increments ofcontrol variables

at time t0

Incrementsof control variables

at time t

Surface rainrate

departure

Adjoint ofForecast

Model(PHYSICS)

Adjoint of spatial

interpolation

Adjoint of MOIST PHYSICS

4DVAR Rainfall Data Assimilation

Forward

Backward

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GPM Validation StrategyTropical Continental

Confidencesanity checks

GPMSatellite

DataStreams

ContinuousSynthesis

∑ error variances∑ precip trends

Calibration

Mid-Lat Continental

Tropical Oceanic

Extratropical Baroclinic

High Latitude Snow

ResearchQuality

Data

Algorith

mIm

provem

ents

Research∑ cloud macrophysics∑ cloud microphysics∑ cloud-radiation modeling

FC Data

Supersite Products

II. GPM Supersites Basic Rainfall Validationhi-lo res gauge/disdrometer networkspolarametric Radar system

Accurate Physical Validationscientists & technicians staffdata acquisition & computer facilitymeteorological sensor systemupfacing multifreq radiometer systemDo/DSD variability/vertical structureconvective/stratiform partitioning

III. GPM Field Campaigns GPM Supersitescloud/ precip/radiation/dynamics processes GPM Alg Problem/Bias Regionstargeted to specific problems

I. Basic Rainfall Validation∑ Raingauges/Radars new/existing gauge networks new/existing radar networks

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Focused Field Campaigns

Meteorology-Microphysics Aircraft

GPM Core Satellite Radar/Radiometer

Prototype Instruments

Piloted

UAVs

150 km

Retrieval Error

Synthesis

AlgorithmImprovement

Guidance

Validation Analysis

Triple Gage Site(3 economy scientific gages)

Single Disdrometer/Triple Gage Site(1 high quality-Large Aperture/2 economy scientific gages)

150 km

100-Gage Site Lo-Res DomainCentered on Multi-parm-Radar

5 km

50-Gage Site Hi-Res DomainCenter-Displaced with

∑ Uplooking Radiom/Radar System[10.7,19,22,37,85,150 GHz/14,35,95 GHz]∑ 915 or 2835 MHz Doppler Radar Profiler

∑  Portable X-band Radar

Data Acquisition-Analysis Facility

DELIVERY

Legend

Multiparameter Radar

Uplk Radiom/Radar940 MHz Profiler

Port X-band Radar

Meteorological Tower

Supersite Template

Site Scientist (3)

Technician (3)

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ECMWF Requirements for GPMfrom Rainfall Assimilation Experience

Spatial Resolution:• Well-defined rain product spatial resolution (ECMWF-model

will be going to 15 km forecast / 30 km assimilation resolutions)Sampling:• Prefer “less often but more accurate”Error Considerations:• Quantification of error in rain detection • Quantification of retrieval errors/time-space biases• Removal of inter-satellite retrieval errors• Assessment of errors due to spatial/temporal sampling mismatch

Plans at ECMWF:• Evaluation of rainrate vs simplified radiance assimilation• Improved estimation of humidity profile forecast errors

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Final Comments

Currently, NASA is only organization with wherewithal to bring online global observing system of precipitation & closely related data assimilation variables which could significantly improve weather forecasting through improvements in water-related components of numerical prediction models & associated data assimilation schemes.

However:

Better observations by themselves do not solve or resolve all standing problems in predictive modeling and thus ESE plan must resolve which groups and through what mechanisms model & data assimilation technique development will proceed to take advantage of current and future space measurements NASA intends to provide.