Evaluation of GPM Precipitation Estimates for Land Data Assimilation Applications
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1 Evaluation of GPM Precipitation Estimates for Land Data Assimilation Applications PI: Prof. Lori Bruce, Ph.D Mississippi State University GeoResources Institute RPC Review (04/14/08) 1
2 GPM Evaluation Team & Collaborators MSU Team Lori Mann Bruce Valentine Anantharaj Georgy Mostovoy Yangrong Ling QiQi Lu Louis Wasson Graduate students External Collaborators Paul Houser (GMU CREW) Joe Turk (Naval Research Laboratories, Monterey, CA) Partner Agencies Garry Schaeffer (USDA NRCS) Steve Hunter (United States Bureau of Reclamation) RPC Review (04/14/08) 2
3 Team Activity MSU GRI: Precipitation evaluation, numerical modeling, project management, RPC Integration. NRL: GPM data, precipitation evaluation. GMU CREW: LSM data assimilation, precipitation evaluation, and science expertise. RPC Review (04/14/08) 3
4 Anticipated Societal Benefits 1. provides critical information to support drought monitoring and mitigation 2. provides essential information for predicting droughts based on weather and climate predictions 3. supports irrigation water management 4. supports fire risk assessment 5. supports water supply forecasting and NWS flood forecasting 6. supplies a critical missing component to assist with snow, climate and associated hydrometeorological data analysis 7. supports climate change assessment 8. enables water quality monitoring 9. supports a wide variety of natural resource management & research activities such as NASA remote sensing activities of soil moisture and ARS watershed studies. RPC Review (04/14/08) 4
5 Precipitation Specifications Depends upon Application Requirements Monthly average rainfall, global (±60o latitude), pentad-type (e.g. 2.5-degree boxes), over land/ocean (Climatic shifts? Desertification?) Accumulated rainfall and snowpack, many stations over a watershed (When do I release water from a reservoir? Allocate water distribution?) ib ti Realtime global or regional analysis of rainrate at the best possible horizontal resolution (hydrological models) 5-minute updates of point rainfall inside an area (e.g., 105 km2) during the lifetime of a thunderstorm or landfalling hurricane away from coastal radars (Should coastal or low-lying areas be evacuated? Temporarily relocate naval fleet to safe harbor?) Any indications that this winter is associated with El Nino conditions? (An energy company, a tree removal company, emergency services) RPC Review (04/14/08) 5
6 GPM Evaluations: Purpose and Activities RPC Review (04/14/08) 6
7 Purpose of RPC GPM Experiments Evaluate usefulness of GPM data for decision support in water resources management and other cross-cutting applications. Test, characterize, and evaluate GPM data in conjunction with other precipitation products in the context of land surface modeling for earth science applications. RPC Review (04/14/08) 7
8 Experimental Objectives of GPM Evaluation 1. Validate space-based precipitation estimation using ground-based d radar and rain gauge data for different cases representing different synoptic condition and surface types 2. Run land surface model experiments to produce water and energy fluxes at 1-10 km resolutions in selected domains, for use in precipitation product evaluation, and subsequent science and application use. 3. Identify water resources applications and metrics to optimize. 4. Define basic standards for error analyses and data quality control by cross correlating the various inputs to the hydrological models. RPC Review (04/14/08) 8
9 Tasks to Achieve Objectives Precipitation Data Processing GPM Proxy data Gage analysis, NEXRAD, other satellite estimates Precipitation it ti Evaluation Technique development Statistical analysis Intra-satellite Sensitivity Analysis Hydrological Modeling Control runs (gage and NEXRAD) Model sensitivity to different precipitation forcings. Analyze results and publish RPC Review (04/14/08) 9
10 GPM Proxy Data RPC Review (04/14/08) 10
11 Multi-Satellite Blending/Merging Techniques for Rapid-Update Precipitation (slides from NRL, NASA, NOAA and other GPM stakeholders) RPC Review (04/14/08) 11
12 Current (10-Satellite) LEO Satellite Constellation Revisit Time Color Codes: SSMI DMSP F-13/14/15 AMSR-E Aqua AMSU-B NOAA-15/16/17 TMI TRMM Coriolis Windsat SSMIS F-16 RPC Review (04/14/08) Revisit Scale: White= 0 hours Black= 6+ hours (shaded boxes represent 15-minute coverage) 12
13 Example from EOS-Terra morning overpass on October 26, Solar zenith is indicated by the white shades and the red stripe indicates the day-night terminator. Note how Terra passes over at nearly the same local time each orbit. RPC Review (04/14/08) Two Major Factors Limit the Quantification of Precipitation from Weather Satellites 1) Revisit Time A single satellite orbits the Earth every 90 minutes and (usually) revisits at nearly the same local l time 2) Beamfilling The structure of the underlying cloud and rainfall is averaged across the antenna beam pattern 13
14 Observing Times for an Ideal Precipitation-Based Low-Earth Orbiting Satellite Observing Constellation 0 Pattern progresses from day to day Orbits are equally spaced with a 1.5 to 3-hour revisit it time Ascending Descending RPC Review (04/14/08) 14
15 What We Have Today: DMSP and NOAA Satellites 0 0 NOAA-17 F-16 F NOAA F-13 F-14 6 NOAA-16 NOAA NOAA Satellites as of Late 2006 DMSP Satellites as of Late 2006 Ascending Descending Ascending Descending RPC Review (04/14/08) 15
16 The Microwave Satellite Beamfilling Problem We don t know the spatial pattern of the underlying rainfall at the time that the satellite flies over Satellite movement Therefore, when one interprets the satellite signal (radiances), there will be a systematic underestimate of the rainfall (e.g, 10 mm/hour) 50-km But it s only raining in this fraction of the sensor s field of view (e.g., 25 mm/hour) RPC Review (04/14/08) Earth s surface Satellite sensor receives a signal for all Earth scenes that fall within this beam ( field of view ) 16 (not drawn to scale)
17 Increasing Refresh and Coverage with Multi-Dataset Merging Techniques Multiple LEO (Microwave) Satellite Merging DMSP orbits Aqua (AMSR) TRMM (TMI+PR) Characteristics Only a few obs per point per day Intermittently y spaced in time Inter-sensor differences (resolution, calibration, algorithm) Open issues: high latitudes, snow, cold/variable surfaces, drizzle RPC Review (04/14/08) 3-hour 6-hour 12-hour 24-hour etc. 17
18 Land Surface Modeling RPC Review (04/14/08) 18
19 Identified Decision Support Needs Routine analysis land surface state (soil moisture, evaporation, land surface temperature) over the continental involves: Observations water soils sun weather climate vegetation terrain Analysis / Modeling observe, model, assimilate Information RPC Review (04/14/08) 19
20 LIS: Scalability 25km External LIS 5km 200 Node LIS Cluster Optimized I/O, GDS Servers Internal Memory Wallclock time CPU time (MB) (minutes) (minutes) LDAS LIS reduction factor km RPC Review (04/14/08) 20
21 Precipitation Datasets RPC Review (04/14/08) 21
22 Sources of Rainfall Data In-situ gage observations RADAR measurements Estimates from satellite data Numerical Weather Prediction Models (NWP) RPC Review (04/14/08) 22
23 Example Precipitation Products Name Domain Space/Time Resolution GEOS 90 N 90 S, 180 W 180 E 1.25x hourly GDAS 90 N 90 S, 180 W 180 E ~ hourly EDAS North America 40 km 3 hourly RUC North America 40 km 3 hourly NRL IR 60 N 60 S, 180 W 180 E 0.25x hourly NRL MW 60 N 60 S, 180 W 180 E 0.25x hourly HUFFMAN 50 N 50 S, 180 W 180 E 0.25x hourly PERSIANN 50 N 50 S, 180 W 180 E 0.25x0.25 Hourly NEXRAD Continental US 4 km Hourly HIGGINS Continental US 0.25x Daily GTS 90 N 90 S, 180 W 180 E Station Data Daily CMAP 90 N 90 S, 180 W 180 E 2.50x2.50 Pentad CMORHP 60 N 60 S, 180 W 180 E 0.073x hourly RPC Review (04/14/08) 23
24 Spatial Domain RPC Review (04/14/08) 24
25 Arkansas Red River Basin RPC Review (04/14/08) 25
26 Arkansas Basin (location of gage sites) RPC Review (04/14/08) 26
27 Other Relevant Activities RPC Review (04/14/08) 27
28 Evaluation Methodologies Implement existing methodologies: Root Mean Squared Error (RMSE) Anomaly Correlation (AC) Equitable Threat Score (ETC) Bias score (BIAS) False Alarm Ratio (FAR) Probability of Detection (POD) Hanssen and Kuipers score (HK) Heidke Skill Score (HSS). Enhance/extend relevant techniques Measure uncertainty of some skill scores Develop new methods Simultaneous evaluation by statistical models RPC Review (04/14/08) 28
29 Sample Early Results RPC Review (04/14/08) 29
30 Daily Precipitation RPC Review (04/14/08) 30
31 Differences in Daily Precipitation when compared to operational product RPC Review (04/14/08) 31
32 RPC Review (04/14/08) 32
33 Land Surface Model Configuration RPC Review (04/14/08) 33
34 Noah/LIS (v. 5.0) Land surface model set up 0.1x0.1 1 latitude-longitude longitude grid
35 Noah/LIS (v. 5.0) Land surface model set up 0.1x0.1 1 latitude-longitude longitude grid
36 Atmospheric forcing fields to run Noah/LIS model North American Land data Assimilation System (NLDAS) Baseline (control) run with NLDAS precipitation Two complete years (2005 and 2006) of spin-up period. The years 2007 and 2008 are used for comparison with GPM synthetic precipitation products Comparison between NLDAS and CMORPH (GPM-Proxy) Forcings ( June 15, 2007, 06 UTC)
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39 Examples of soil moisture simulated by Noah/LIS (control run) model June 15, 2007, 18 UTC (11-12 am of local time) GPM domain
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41 Task ID Schedule Task Description Data collection and inventory 1.2 Full evaluation of precipitatation data 1.3 Error estimation 2.1 Regional validation 3.1 LIS Scontrol o simulations uato satco core estes sites 3.2 LIS simulations with different preciptation forcings 3.3 LIS validation against in-situ data 4.1 Document and report results RPC Review (04/14/08) 41
42 Contact Information Valentine Anantharaj edu> Tel: (662) RPC Review (04/14/08) 42
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