Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.

Optimal soil moisture estimation may be characterized by intercomparisons among remotely sensed measurements, ground‐based measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSR‐E), ground‐based measurements, and a Soil‐Vegetation‐Atmosphere Transfer (SVAT) model for the Soil Moisture Experiments in 2003 (SMEX03) Little River region, Georgia. The Common Land Model (CLM) reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases (0.001–0.054 m3/m3) as compared to AMSR‐E and ground data. While the AMSR‐E average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. The comparison results showed that highest mean absolute error (MAE) and root mean squared error (RMSE) were 0.054 and 0.059 m3/m3 for short and long periods, respectively. Even if CLM and AMSR‐E had complementary strengths, low MAE (0.018–0.054 m3/m3) and RMSE (0.023–0.059 m3/m3) soil moisture errors for CLM and soil moisture low biases (0.003–0.031 m3/m3) for AMSR‐E, care should be taken prior to employing AMSR‐E retrieved soil moisture products directly for hydrological application due to its failure to replicate temporal variability. AMSR‐E error characteristics identified in this study should be used to guide enhancement of retrieval algorithms and improve satellite observations for hydrological sciences

Radiation measurements from polar and geosynchronous satellites

The following topics are discussed: (1) cloud effects in climate determination; (2) annual variation in the global heat balance of the earth; (3) the accuracy of precipitation estimates made from passive microwave measurements from satellites; (4) seasonal oceanic precipitation frequencies; (5) determination of mesoscale temperature and moisture fields over land from satellite radiance measurements; and (6) Nimbus 6 scanning microwave spectrometer data evaluation for surface wind and pressure components in tropical storms

LMODEL: A satellite precipitation methodology using cloud development modeling. Part I: Algorithm construction and calibration

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (similar to 4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear

Studies of satellite support to weather modification in the western US region

The applications of meteorological satellite data to both summer and winter weather modification programs are addressed. Appraisals of the capability of satellites to assess seedability, to provide real-time operational support, and to assist in the post-experiment analysis of a seeding experiment led to the incorporation of satellite observing systems as a major component in the Bureau of Reclamations weather modification activities. Satellite observations are an integral part of the South Park Area cumulus experiment (SPACE) which aims to formulate a quantitative hypothesis for enhancing precipitation from orographically induced summertime mesoscale convective systems (orogenic mesoscale systems). Progress is reported in using satellite observations to assist in classifying the important mesoscale systems, and in defining their frequency and coverage, and potential area of effect. Satellite studies of severe storms are also covered

Determination of atmospheric moisture structure and infrared cooling rates from high resolution MAMS radiance data

This program has applied Multispectral Atmospheric Mapping Sensor (MAMS) high resolution data to the problem of monitoring atmospheric quantities of moisture and radiative flux at small spatial scales. MAMS, with 100-m horizontal resolution in its four infrared channels, was developed to study small scale atmospheric moisture and surface thermal variability, especially as related to the development of clouds, precipitation, and severe storms. High-resolution Interferometer Sounder (HIS) data has been used to develop a high spectral resolution retrieval algorithm for producing vertical profiles of atmospheric temperature and moisture. The results of this program are summarized and a list of publications resulting from this contract is presented. Selected publications are attached as an appendix

A Soil-Canopy-Atmosphere Model for Use in Satellite Microwave Remote Sensing

Regional and global scale studies of land-surface-atmosphere interactions require the use of observations for calibration and validation. In situ field observations are not representative of the distributed nature of land surface characteristics, and large-scale field experiments are expensive undertakings. In light of these requirements and shortcomings, satellite observations serve our purposes adequately. The use of satellite data in land surface modeling requires developing a hydrological model with a thin upper layer to be compatible with the nature of the satellite observations and that would evaluate the soil moisture and soil temperature of a thin layer close to the surface. This paper outlines the formulation of a thin layer hydrological model for use in simulating the soil moistures and soil temperatures. This thin layer hydrological model is the first step in our attempt to use microwave brightness temperature data for regional soil moisture estimation. The hydrological model presented here has been calibrated using five years (1980–1984) of daily streamflow data for the Kings Creek catchment. The calibrated parameters are used to validate the daily streamflows for the next 5 year period (1985–1989). The comparison of surface soil moistures and surface temperatures for the period of the Intensive Field Campaigns (IFCs) during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) in 1987 is carried out and yields good results. The thin layer hydrological model is coupled with a canopy radiative transfer model and an atmospheric attenuation model to create a coupled soil-canopy-atmosphere model in order to study the effect of the vegetation and the soil characteristics on the Special Sensor Microwave Imager (SSM/I) brightness temperatures. The sensitivities of the brightness temperatures to the soil and vegetation is examined in detail. The studies show that increasing leaf area index masks the polarization difference signal originating at the soil surface

Quantifying land surface temperature variability for two Sahelian mesoscale regions during the wet season

Land-atmosphere feedbacks play an important role in the weather and climate of many semi-arid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. We used spectral analysis to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth Observations (EO). We analysed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (< 5–day) spectral variance, notably a shift towards lower frequency variability in forest pixels relative to non-forest areas, and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, we found that areas of forest, and to a lesser extent grassland regions, warm up more slowly than sparsely vegetated or barren pixels. We applied the same spectral analysis method to simulated LST data from the the Joint UK Land Environment Simulator (JULES) land surface model. We found a reasonable level of agreement with the EO spectral analysis, for two contrasting land surface regions. However JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EO. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events

Evaluation of Long-Term SSM/I-Based Precipitation Records over Land

The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products