Matthias Drusch

AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP-MEM

This paper presents the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Models Intercomparison Project (ALMIP) for Microwave Emission Models (ALMIP-MEM). ALMIP-MEM comprises an ensemble of simulations of C-band brightness temperatures over West Africa for 2006. Simulations have been performed for an incidence angle of 55°, and results are evaluated against C-band satellite data from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E). The ensemble encompasses 96 simulations, for 8 Land Surface Models (LSMs) coupled to 12 configurations of the Community Microwave Emission Model (CMEM). CMEM has a modular structure which permits combination of several parameterizations with different vegetation opacity and soil dielectric models. ALMIP-MEM provides the first intercomparison of state-of-the-art land surface and microwave emission models at regional scale. Quantitative estimates of the relative importance of land surface modeling and radiative transfer modeling for the monitoring of low-frequency passive microwave emission on land surfaces are obtained. This is of high interest for the various users of coupled land surface microwave emission models. Results show that both LSMs and microwave model components strongly influence the simulated top of atmosphere (TOA) brightness temperatures. For most of the LSMs, the Kirdyashev opacity model is the most suitable to simulate TOA brightness temperature in best agreement with the AMSR-E data. When this best microwave modeling configuration is used, all the LSMs are able to reproduce the main temporal and spatial variability of measured brightness temperature. Averaged among the LSMs, correlation is 0.67 and averaged normalized standard deviation is 0.98.

Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002

Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

Assimilation of Screen-Level Variables in ECMWF’s Integrated Forecast System: A Study on the Impact on the Forecast Quality and Analyzed Soil Moisture

Abstract In many operational numerical weather prediction applications, the soil moisture analysis is based on the modeled first-guess and screen-level variables; that is, 2-m temperature and 2-m relative humidity. A set of two global 61-day analysis/forecast experiments based on the Integrated Forecast System at the European Centre for Medium-Range Weather Forecasts (ECMWF) has been performed for June and July 2002. Analyses and forecasts based on the operational Optimal Interpolation (OI) scheme are compared against results obtained from an open loop system, in which soil moisture evolves freely. It is found that soil moisture assimilation or analysis has a significant impact on the model atmosphere. Temperature forecasts for the Northern Hemisphere up to a level of 700 hPa and up to nine days were significantly improved when the operational analysis was used. A comparison of volumetric soil moisture against in situ observations from the Oklahoma Mesonet reveals, however, that the operational OI system ...

ECMWF's Global Snow Analysis: Assessment and Revision Based on Satellite Observations

Abstract Snow water equivalent and snow extent are key parameters for the earths energy and water budget. In this study, the current operational snow-depth analysis (2D spatial Cressman interpolation) at the European Centre for Medium-Range Weather Forecasts (ECMWF), which relies on real-time observations of snow depth, the short-range forecast, and snow-depth climatic data, is presented. The operational product is compared with satellite-derived snow cover. It is found that the total area of grid boxes affected by snow is approximately 10% larger in the analysis than in the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) snow-extent product. The differences are persistent in time and space and cover the entire Northern Hemisphere. They comprise areas with intermittent and/or patchy snow cover, for example, the Tibetan Plateau, the edges of snow fields, and areas with a low density of observations, which are difficult to captur...

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these...

Up‐scaling effects in passive microwave remote sensing: ESTAR 1.4 GHz measurements during SGP '97

Passive microwave radiation measurements over sparsely vegetated surfaces during the Southern Great Plain Experiment are used to determine spatial up-scaling effects. For various distributions of geophysical parameters, the differences between mean soil moisture derived from high resolution (800 by 800m²) 1.4 GHz brightness temperatures and soil moisture calculated from mean brightness temperatures at larger scales are compared. For spatial resolutions of 31.4²km², the effects of these non-linearities in radiative transfer at 1.4 GHz do not yield differences in estimated volumetric surface soil moisture (0–5 cm) exceeding 0.02 [cm³cm−3].

The impact of the SSM/I antenna gain function on land surface parameter retrieval

In order to investigate the effect of the antenna gain on land surface parameter retrievals with SSM/I, a Gauss-function is fitted to the 19 GHz antenna gain functions. Soil moisture and surface temperature distributions for the Red-Arkansas River basins are calculated from a soil-vegetation-atmosphere model. A passive microwave emission model is applied to derive polarized 19 GHz brightness temperatures. Those parameters are aggregated up to 19 GHz footprint size taking into account different spatial weighting according to the Gauss-function. These reference averages represent the full radiometer resolution, which is approximately three times the −3dB resolution. The comparisons between the reference values and simple linear averages for several spatial resolutions lead to differences varying from 2% to 17% volumetric soil moisture.

Soil Moisture Remote Sensing for Numerical Weather Prediction: L-Band and C-Band Emission Modeling Over Land Surfaces, the Community Microwave Emission Model (CMEM)

The community microwave emission model (CMEM) is the low frequency forward observation operator developed at ECMWF. It is used in this paper to simulate brightness temperatures at local and regional scales over SMOSREX (France) and AMMA (West Africa), respectively. Background errors in simulated brightness temperatures are quantified at different frequencies and incidence angles for these two sites.

Soil moisture retrieval over the southern Great Plains: comparisons between experimental remote sensing data and operational products

The southern Great Plains region of the US has been a focus area for experimental remote sensing of surface soil moisture since the 1970s. Intercomparison of soil moisture retrieval using both experimental data and operational data is carried out during the SGP99 remote sensing campaign in July 1999. Passive microwave measurements obtained from the airborne ESTAR instruments at L-band and TRMM microwave imager (TMI) measurements at X-band were processed to retrieve surface soil moisture during SGP99 and compared to field measurements. TMI retrieved soil moisture for June-September 1999 were compared with operational soil moisture sensors in the Oklahoma (OK) Mesonet system. To mimic operational products, the correction for vegetation and surface roughness in the TMI retrievals are based on average literature values, and surface temperatures estimated from a land surface hydrologic model forced with operational products.