Jared K. Entin

The Global Land Data Assimilation System

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employe...

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

The Global Soil Moisture Data Bank

Abstract Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil—moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stat...

Temporal and spatial scales of observed soil moisture variations in the extratropics

Scales of soil moisture variations are important for understanding patterns of climate change, for developing and evaluating land surface models, for designing surface soil moisture observation networks, and for determining the appropriate resolution for satellite-based remote sensing instruments for soil moisture. Here we take advantage of a new archive of in situ soil moisture observations from Illinois and Iowa in the United States, and from Russia, Mongolia, and China, to evaluate the observed temporal and spatial scales of soil moisture variations. We separate the variance into two components, the very small scale, determined by soils, topography, vegetation, and root structure, and the large scale forced by the atmosphere. This larger scale, determined by precipitation and evaporation patterns, is of interest for global climate modeling. We characterize the small scale as white noise for our analysis, keeping in mind that it is an important component of soil moisture variations for other problems. We find that the atmospheric spatial scale for all regions is about 500 km. The atmospheric temporal scale is about 2 months for the top 1-m soil layer. The temporal scale for the top 10-cm layer is slightly less than 2 months. The white noise component of the variance for temporal variations ranges from 50% for the top 10 cm to 20–40% for the top 1 m. For spatial variations the white noise component is the same for all depths but varies with region from 30% for Illinois to around 70% for Mongolia. Nevertheless, the red noise (atmospheric component) can be seen in all regions. These results are for Northern Hemisphere midlatitudes and would not necessarily apply to other latitudes. The results are based on observations taken from grassland or agricultural areas, and may not be similar to those of areas with other vegetation types. In China, a region with substantial latitudinal variation, the temporal scale for the top 1 m varies from 1 month in the south to 2.5 months in the north, demonstrating the control of potential evaporation on the temporal scales. Seasonal analysis of the scales of soil moisture for Illinois shows that during the winter the temporal scales are long, though the spatial scales are short. We suggest that these variations are both attributable to the seasonal cycle of potential evaporation.

The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d)

Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra- and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models’ snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.

Simulations of a Boreal Grassland Hydrology at Valdai, Russia: PILPS Phase 2(d)

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966‐83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

Satellite remote sensing of soil moisture in Illinois, United States

To examine the utility of using satellite passive microwave observations to measure soil moisture over large regions, we conducted a pilot study using the scanning multichannel microwave radiometer (SMMR) on Nimbus-7, which operated from 1978 to 1987, and actual in situ soil moisture observations from the state of Illinois, United States, which began in 1981. We examined SMMR midnight microwave brightness temperatures on a 0.5° × 0.5° grid, and compared them with direct soil moisture measurements at 14 sites in Illinois for the period 1982–1987. The results suggest that both the polarization difference and the microwave emissivity for horizontal polarization at frequencies ≤18 GHz have real utility for use as a soil moisture information source in regions with grass or crops where the vegetation is not too dense. While SMMR observations ended in 1987, special sensor microwave/imager observations at 19 GHz start then and extend to the present, and advanced microwave scanning radiometer instruments will fly on satellites beginning soon. Together with SMMR, they have the potential to produce a soil moisture record over large regions for more than two decades and extend it into the future. Satellite observations from these low-resolution satellite instruments measure the component of large-scale long-term soil moisture variability that is related to atmospheric forcing (from precipitation, evapotranspiration, and snowmelt).

International Global Precipitation Measurement (GPM) Program and Mission: An Overview

Eric A. Smith , Ghassem Asrar , Yoji Furuhama , Amnon Ginati , Christian Kummerow , Vincenzo Levizzani , Alberto Mugnai , Kenji Nakamura , Robert Adler , Vincent Casse , Mary Cleave , Michele Debois , John Durning , Jared Entin , Paul Houser , Toshio Iguchi , Ramesh Kakar , Jack Kaye , Masahiro Kojima , Dennis Lettenmaier , Michael Luther , Amita Mehta , Pierre Morel , Tetsuo Nakazawa , Steven Neeck , Ken’ichi Okamoto , Riko Oki , Garudachar Raju , Marshall Shepherd , Erich Stocker , Jacques Testud , and Eric Wood 19

Optimal design of surface networks for observation of soil moisture

By analyzing in situ soil moisture data, we show that soil moisture variability consists of two components, one of which is related to large-scale atmospheric forcing, and the other related to small-scale land surface variability and hydrologic processes. We use empirically estimated spatial autocorrelation functions for Illinois to estimate errors of spatial averaging of soil moisture observations, using the method of statistically optimal averaging of meteorological fields. The estimated dependence of the root-mean-square errors of averaging on the soil moisture station network density can be used to analyze existing observational networks and for designing new ones. For the application of providing information on a regular grid for numerical models of weather and climate, we show that the new, relatively high density networks of soil moisture observations in Oklahoma, may not provide estimates with very much more accuracy than the relatively low density currently operational network in Illinois. This prediction must be tested when we receive sufficiently long time series of observations from Oklahoma.

The Soil Moisture Active/Passive Mission (SMAP)

The Soil Moisture Active/Passive (SMAP) mission will deliver global views of soil moisture content and its freeze/thaw state that are critical terrestrial water cycle state variables. Polarized measurements obtained with a shared antenna L-band radar and radiometer system will allow accurate estimation of soil moisture at hydrometeorological scale (10 km) and hydroclimatological scale (40 km) resolutions. The sensors will share a feed and a deployable light-weight mesh reflector that will make conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of soil moisture and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive measurements enables global soil moisture mapping with unprecedented resolution, sensitivity, area coverage, and revisit. This paper outlines the science objectives of the SMAP mission and provides an overview of the measurement approach and data products.