H33I-01 INVITED
The Soil Moisture Active/Passive Mission (SMAP)
The National Research Council's decadal survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, was released in 2007 as the culmination of a two year study commissioned by NASA, NOAA, and USGS to provide consensus recommendations to guide the agencies" space-based Earth observation programs in the coming decade. The report committee sent out a Request for Information call to the community and received over one hundred mission concepts. The committee and its panels ultimately recommended seventeen priority missions for implementation in several time-blocks in the coming decade. The Soil Moisture Active/Passive (SMAP) mission was the highest priority mission of the Panel on Water Resources and the Global Hydrologic Cycle and the report recommended it for implementation in the first phase of missions (2010-2013). The mission will enable global soil moisture mapping with unprecedented resolution, sensitivity, area coverage, and revisit. SMAP draws heavily upon the heritage of the Hydrosphere State (Hydros) mission which was cancelled due to budget constraints in late 2005. NASA Headquarters held a two-day workshop on July 9-10, 2007 to evaluate the SMAP mission as defined in the report and to identify the ancillary measurements (if any) required to accomplish mission goals. A report on the workshop has been prepared. This presentation reports on the conclusions of the workshop in response to the charge by NASA Headquarters. http://hydrology.jpl.nasa.gov/events/SMAPpresentations/
H33I-02
A Study on Soil Moisture Estimation with AMSR-E
The passive microwave satellite measurements are available from AMSR-E on AQUA EOS-PM. They provide the measurements with the large incidence angle of 55° and with the multi-frequency and polarization for monitoring the geophysical properties of the land, ocean surfaces and atmosphere. We will show our current improvements on soil moisture retrieval algorithm, that include two major parts: For ground surface component, we 1) evaluated the AIEM model for its ability in the applications to the current available multi-frequency and high incidence sensor; 2) developed a parameterized surface emission model using the database simulated by the AIEM model for a wide range of surface roughness and soil moisture conditions under the AMSR-E sensor configurations; and 3) developed an inversion model for surface soil moisture and roughness estimation. For vegetation component, we developed a technique that could be used to separate ground surface and vegetation signals in the satellite measurements. The vegetation signals lead to a new capability for monitoring global vegetation properties and provide the very useful information on vegetation correction when estimating soil moisture. We will demonstrate our improvements on soil moisture estimation with AMSR-E observations in detail and discuss the related research issues.
H33I-03
Soil moisture estimation using WindSat based passive microwave polarimetric observations
Global soil moisture estimates are critical to study its role in weather and climate. Microwave remote sensing is the most feasible technique for large-scale soil moisture observations. Efforts have been made towards the goal of obtaining accurate satellite-based soil moisture products. Low frequencies (1.4 GHz) are preferable for soil moisture retrieval since perturbing factors such as vegetation are less significant. WindSat is a spaceborne multi- frequency polarimetric microwave radiometer operating at 6.8, 10.7, 18.7, 23.8 and 37.0 GHz. WindSat covers a 1025 km swath at an incidence angle of 53 degrees. WindSat has a sun synchronous polar orbit with a descending node at 6:00 AM. There is a growing interest of using WindSat observations for retrieving land variables. In this study, single channel algorithm is applied to WindSat data to estimate global soil moisture. This is the first attempt to do global estimates of soil moisture from WindSat observations. These estimates will complement soil moisture estimates from AMSR-E observations. Comprehensive validation work has been done by comparing the retrievals with in situ soil moisture observations from the networks at four carefully designed satellite soil moisture validation sites. The four watersheds have very different climate and vegetation conditions. The overall SEE of soil moisture for the four watersheds is 0.038 m3/m3. Analysis shows that WindSat soil moisture retrievals for all four validation sites are reasonable with acceptable error bounds. Soil moisture estimates for both ascending and descending orbits were reasonable. The spatial distribution of soil moisture was consistent with the known global climatology.
H33I-04
Virtual Mission First Results Supporting the WATER HM Satellite Concept
Surface fresh water is essential for life, yet we have surprisingly poor knowledge of its variability in space and time. Similarly, ocean circulation and ocean-atmosphere interactions fundamentally drive weather and climate variability, yet the global ocean current and eddy field (e.g., the Gulf Stream) that affects ocean circulation is poorly known. The Water And Terrestrial Elevation Recovery Hydrosphere Mapper satellite mission concept (WATER HM or SWOT per the NRC Decadal Survey) is a swath-based interferometric-altimeter designed to acquire elevations of ocean and terrestrial water surfaces at unprecedented spatial and temporal resolutions. WATER HM will have tremendous implications for estimation of the global water cycle, water management, ocean and coastal circulation, and assessment of many water-related impacts from climate change (e.g., sea level rise, carbon evasion, etc.). We describe a hydrological "virtual mission" (VM) for WATER HM which consists of: (a) A hydrodynamic-instrument simulation model that maps variations in water levels along river channels and across floodplains. These are then assimilated to estimate discharge and to determine trade-offs between resolutions and mission costs. (b) Measurements from satellites to determine feasibility of existing platforms for measuring storage changes and estimating discharge. First results demonstrate that: (1) Ensemble Kalman filtering of VM simulations recover water depth and discharge, reducing the discharge RMSE from 23.2% to 10.0% over an 84- day simulation period, relative to a simulation without assimilation. The filter also shows that an 8-day overpass frequency produces discharge relative errors of 10.0%, while 16-day and 32-day frequencies result in errors of 12.1% and 16.9%, respectively. (2) SRTM measurements of water surfaces along the Mississippi, Missouri, Ohio, and Amazon rivers, as well as smaller tributaries, show height standard deviations of 5 meters or greater (SRTM is the heritage for WATER HM). These large errors require several hundred kilometer reach lengths to estimate slope and hence discharge in the empirical Manning's method. Nevertheless, discharge estimates are reasonable and can be within 10% of gauged values. (3) River channel widths are key for determining the capability of WATER HM to resolve flow hydraulics. Automated measurements of channels, as classified in NLCD92 (a 30m product from the USGS Land Cover Institute), show detailed coverage throughout the Ohio River Basin, including channels with annual discharge of 150 cms, draining 12,500 sqkm. (4) Conventional profiling altimetry misses 75% of all lakes in the world because there are hundreds of kilometers between orbital tracks. This is highly problematic for understanding storage changes in Arctic lakes which are disappearing but in a spatially varying, heterogeneous manner. http://earthsciences.osu.edu/water
H33I-05
Current Measurements in Rivers by Spaceborne Along-Track Interferometric Synthetic Aperture Radar
The along-track interferometric synthetic aperture radar (along-track InSAR) technique permits a high-resolution imaging of ocean surface current fields all over the world from satellites. Results of the Shuttle Radar Topography Mission (SRTM) in early 2000 and theoretical findings indicate that spaceborne along-track InSARs are also suitable for current retrievals in rivers if the water surface is at least 200-300 m wide and sufficiently rough for microwave backscattering at slanting incidence. Accordingly, the technique is quite attractive for global river runoff monitoring, where it can complement water level and surface slope measurements by advanced radar altimeters and other efforts. The German satellite TerraSAR-X, which was launched in June 2007, will permit along-track interferometry in an experimental mode of operation. This will be the first opportunity for repeated current measurements from space at selected test sites during a period of several years. In this presentation we give an overview of basic principles and theoretical limits of current measurements by along-track InSAR, example results from SRTM, and predicted along-track InSAR capabilities of TerraSAR-X. An SRTM-derived surface current field in the lower Elbe river (Germany) agrees well with numerical hydrodynamic model results; characteristic lateral current variations around a pronounced main flow channel in the 1500 m wide river are resolved. Despite clearly suboptimal instrument parameters, TerraSAR-X simulations indicate an even better data quality. Depending on width, surface roughness, and relative flow direction of a river, current estimates with an accuracy better than 0.1 m/s will be possible with an effective spatial resolution of a few hundred meters to kilometers.
H33I-06 INVITED
Quantifying the Global Fresh Water Budget: Capabilities from Current and Future Satellite Sensors
The global water cycle is complex and its components are difficult to measure, particularly at the global scales and with the precision needed for assessing climate impacts. Recent advances in satellite observational capabilities, however, are greatly improving our knowledge of the key terms in the fresh water flux budget. Many components of the of the global water budget, e.g. precipitation, atmospheric moisture profiles, soil moisture, snow cover, sea ice are now routinely measured globally using instruments on satellites such as TRMM, AQUA, TERRA, GRACE, and ICESat, as well as on operational satellites. New techniques, many using data assimilation approaches, are providing pathways toward measuring snow water equivalent, evapotranspiration, ground water, ice mass, as well as improving the measurement quality for other components of the global water budget. This paper evaluates these current and developing satellite capabilities to observe the global fresh water budget, then looks forward to evaluate the potential for improvements that may result from future space missions as detailed by the US Decadal Survey, and operational plans. Based on these analyses, and on the goal of improved knowledge of the global fresh water budget under the effects of climate change, we suggest some priorities for the future, based on new approaches that may provide the improved measurements and the analyses needed to understand and observe the potential speed-up of the global water cycle under the effects of climate change.
H33I-07
The Passive Microwave Water Cycle (PMWC) Product: Closing the Water Cycle Using a Constellation of Satellites
We have developed a water cycle product as part of the NASA Energy and Water Cycle Study (NEWS). The purpose of the product is to integrate passive microwave retrievals from a variety of different sensors on different satellites including SSMI (F08, F10, F11, F13, F14, and F15), SSMIS (F16 and F17), AMSR (Aqua and Midori-II), TMI on TRMM, WindSat, and eventually AMSU (NOAA-15 and NOAA-16). The water cycle over a particular location averaged over a time scale of one month is given by: E-P=WVTD; where E is evaporation, P is precipitation, and WVTD is water vapor transport divergence. The new and unique feature of our product is that we make use of the large quantity of accurately intercalibrated water vapor and wind observations in order to estimate WVTD. As part of developing this product we have evaluated our new intercalibrated microwave rain rates, developed a procedure for estimating evaporation, and developed a procedure for estimating water vapor transport and its divergence. The Version-01 Passive Microwave Water Cycle (PMWC) dataset will contain maps of evaporation, precipitation, water vapor transport, water vapor transport divergence, and water vapor. Uncertainty estimates for each parameter will also be supplied. Currently, the product is a 20-year (1987-2007), 0.25-degree, monthly average product over the global oceans. One of our principle motivations is to obtain estimates of the uncertainty in "direct" physically-based retrievals of precipitation. Direct physically-based rain retrievals are subject to large uncertainties that are hard to quantify, such as horizontal inhomogeneity (beamfilling), cloud and rain water partitioning, rain column height and the rain vertical profile, drop size distribution, and the effects of frozen hydrometeors. By using the balanced water cycle, we can estimate precipitation uncertainties in P by estimating uncertainties in E and WVTD. Estimating uncertainties in E can be done with a straight-forward classical uncertainty analysis. Uncertainties in WVTD can easily be estimated using on-orbit simulation experiments with model data. In addition to uncertainty estimates, we also note that estimating precipitation through balancing the water cycle provides a new and independent estimate of precipitation. The errors with this technique are independent from the errors in passive microwave, active microwave, and infrared precipitation retrievals. This will be of great value as we enter the Global Precipitation Measurement (GPM) era. Moreover, consistency among hydrological parameters (evaporation, precipitation, water vapor transport divergence) and especially their trends provides indirect validation of the retrievals. Currently, we have evaluated trends in our evaporation, precipitation, and water vapor datasets on a global average basis. We find water vapor trends of 7 Percent/degree as the world warms – consistent with the Clausius-Clapeyron (C-C) relationship and with climate models. We also find that our evaporation and precipitation measurements both increase at close to the C-C rate. This is in contrast with climate models that predict a muted response of precipitation to global warming with rates between 1 to 3 Percent/degree. These water cycle relationships will be discussed in terms of their implications for the global energy balance.
H33I-08
A Merged Satellite Atmospheric Data Set for Hydrology and Climate Studies
Several of the instruments in the NASA A-Train satellite constellation observe atmospheric water quantities. These instruments included the Atmospheric Infrared Sounder (AIRS), the Advance Microwave Scanning Radiometer for EOS (AMSR-E) and the Moderate-resolution Imaging Spectroradiometer (MODIS) all on Aqua, the Microwave Limb Sounder (MLS) on Aura, the Cloudsat radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar. AIRS, AMSR-E, MODIS and MLS measure water vapor, while CloudSat, CALIPSO, MLS AMSR-E, MODIS and AIRS observe a variety of cloud properties, including fraction, top height and bottom and cloud ice and water distributions. Because these satellites fly in formation as part of the A-Train, these measurements are made with overlapping spatial coverage and time coincidence of a few minutes or less. These sampling characteristics preserve the instantaneous relationship between water vapor, cloud liquid, and cloud ice. We are combining these observations into a long-term data record as part of NASA's Energy and Water Cycle Study (NEWS) program. The merged data set is providing observational constraints on numerical models of the hydrologic cycle. Some of the challenges inherent in this work include reconciling similar quantities observed by different instruments, placing observations from different sampling grids into useful formats, merging data sets with different height coverage, and distilling relevant quantities from very large data sets of several years' duration. We also show long-term variability of water vapor observed with this data set.