Multi-mission synergistic activities: A new era of integrated missions

Christa Peters-Lidard

Deputy Director, Hydrospheric and Biospheric Sciences,

Goddard Space Flight Center

Earth Science Missions

Distributed Hydrologic and Land Surface

NWP/Data Assimilation

• Characterization of uncertainties in satellite and ground-based (radar, dense gauge networks) rainfall estimates over a broad range of space/time scales

• Characterization of uncertainties in hydrologic models and understanding propagation of input uncertainties into model forecasts

• Assessing performance of satellite rainfall products in hydrologic applications over a range of space-time scales

• Using data from synergistic missions (e.g. SMOS, SMAP, GRACE) to refine hydrologic model parameters and improve predictions driven by GPM input data

Assessing the performance of satellite precipitation products in hydrologic and water resources applications and characterization of

model and observation errors

Integrated Hydrologic Validation

PMM Hydrology Working Group Activities

1. How can we improve current and future PMM products?– How can PMM precipitation products be downscaled

spatially/temporally using ancillary data (VIS/IR, gauge, climatology, CAPE, orography)?

– How can other RS products be used to reduce accuracy requirements and/or constrain uncertainty?

– Can land surface characterization and integrated hydrological validation help improve/correct retrievals?

2. How useful are current and future PMM products for hydrological science and applications?– What are the key characteristics (e.g., bias, POD, FAR, PDFs) and

their accuracy requirements at various spatial and temporal scales? – Can we develop (a) common framework(s) to demonstrate this for

various applications (floods, agriculture/food, land surface modeling/soil moisture/drought, landslides, etc.)?

– How do current and how will future PMM product accuracy compare to NWP analysis products with regard to usefulness for hydrology?

GPM-SMAP Synergy 1: Product evaluation

• SMAP Soil Moisture assimilated into a simple model could provide an independent estimate of GPM errors in data-poor regions as in Crow and Bolten, 2007

GPM-SMAP Synergy 2: Improve products•SMAP soil moisture and temperature assimilated into a land surface model could improve GPM precipitation products (Wanders et al., RSE, 2015)

Skill scores of TMPA-RT and TMPA-RT after assimilation of soil moisture, land surface temperature or both. Skill scores are calculated based on comparison with ground-based precipitation from NLDAS-2 with a 0 mm rain threshold to distinguish between a rain event and dry conditions.

Dry soil

SMAP soil moisture can be used to diagnose the “regime” of microwave emissivity for dry vs. wet soils, and thereby improve retrievals as in Ringerud et al., 2014

SGP

HMT-E

Amazon

Wet soil

GPM-SMAP Synergy 3: Improve retrievals

GPM-SMAP Synergy 4: Joint retrievals•Calibrating a forward emissivity model with GMI emissivities can improve also improve soil moisture estimates (Harrison et al, 2015)

Soi

l Moi

stur

e (v

ol/v

ol)

BLUE: Default Noah RED: Noah 3.3-CMEM calibrated to 10.65V/H AMSR-E retrievals of land surface emissivity (one warm season’s worth)*BLACK: Observations

Date in 2007

Figure 1: Difference in (a) AMSR-E LPRM soil moisture, (b) MODIS MCD43C3 white sky albedo, (c) NU-WRF top 10cm soil moisture, and (d) NU-WRF surface albedo between July 2006 and July 2007. Box in (b) indicates the NU-WRF modeling domain.

Figure 2: Cumulative precipitation for four month simulations with NU-WRF that have no soil moisture memory (NSM), standard soil moisture memory (SMM), and soil moisture memory with active albedo (SMA), compared to observed precipitation from NLDAS for the drought-affected Southern Great Plains. Simulations with soil moisture memory are significantly closer to NLDAS.

Ob

serv

edM

od

eled

Soil Moisture Albedo

GPM-SMAP Synergy 5: Coupled physics

Summary: GPM-SMAP Synergies

1. Product Evaluation2. Improving products3. Improving retrievals4. Joint retrievals5. Land-atmosphere interactions &

microphysics

Future Synergies

1. GRACE-FO (2017) and GRACE-2– Monthly water budgets

2. ICESat-2 (2017)– Surface water and snow products

3. SWOT (2020)– Surface water/discharge products