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Daniel B. Wright
,
Dalia B. Kirschbaum
, and
Soni Yatheendradas

Abstract

Satellite multisensor precipitation products (SMPPs) have a variety of potential uses but suffer from relatively poor accuracy due to systematic biases and random errors in precipitation occurrence and magnitude. The censored, shifted gamma distribution (CSGD) is used here to characterize the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis (TMPA), a commonly used SMPP, and to compare it against the rain gauge–based North American Land Data Assimilation System phase 2 (NLDAS-2) reference precipitation dataset across the conterminous United States. The CSGD describes both the occurrence and the magnitude of precipitation. Climatological CSGD characterization reveals significant regional differences between TMPA and NLDAS-2 in terms of magnitude and probability of occurrence. A flexible CSGD-based error modeling framework is also used to quantify errors in TMPA relative to NLDAS-2. The framework can model conditional bias as either a linear or nonlinear function of satellite precipitation rate and can produce a “conditional CSGD” describing the distribution of “true” precipitation based on a satellite observation. The framework is also used to “merge” TMPA with atmospheric variables from version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) to reduce SMPP errors. Despite the coarse resolution of MERRA-2, this merging offers robust reductions in random error due to the better performance of numerical models in resolving stratiform precipitation. Improvements in the near-real-time version of TMPA are relatively greater than for the higher-latency research version.

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Kristi R. Arsenault
,
Grey S. Nearing
,
Shugong Wang
,
Soni Yatheendradas
, and
Christa D. Peters-Lidard

Abstract

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.

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Amy McNally
,
Gregory J. Husak
,
Molly Brown
,
Mark Carroll
,
Chris Funk
,
Soni Yatheendradas
,
Kristi Arsenault
,
Christa Peters-Lidard
, and
James P. Verdin

Abstract

The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

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