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Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT. [ABSTRACT FROM AUTHOR]

This article discusses the advancements in precipitation retrieval and applications from low-Earth-orbiting (LEO) satellite information. The workshop, sponsored by the Center for Hydrometeorology and Remote Sensing, brought together scientists and stakeholders to discuss the state of the science and users' needs for operational precipitation algorithms and products. The workshop covered various topics, including observation system requirements, algorithm development, and the uncertainties associated with satellite data. The recommendations from the workshop emphasized the need for collaboration, accurate calibration of sensors, and the development of robust algorithms to improve the accuracy of global precipitation products. [Extracted from the article]

Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992-2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets. DOI: 10.1175/2009JAMC2294.1

Cloud droplet effective radius (DER) and liquid water path (LWP) are two key parameters for the quantitative assessment of cloud effects on the exchange of energy and water. Chang and Li presented an algorithm using multichannel measurements made at 3.7, 2.1, and 1.6 [micro]m to retrieve a cloud DER vertical profile for improved cloud LWP estimation. This study applies the multichannel algorithm to the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data on the Aqua satellite, which also carries the Advanced Microwave Scanning Radiometer (AMSR-E) for measuring cloud LWP and precipitation. By analyzing one day of coincident MODIS and AMSR-E observations over the tropical oceans between 40[degrees]S and 40[degrees]N for overcast warm clouds (>273 K) having optical depths between 3.6 and 23, the effects of DER vertical variation on the MODIS-derived LWP are reported. It is shown that the LWP tends to be overestimated if the DER increases with height within the cloud and underestimated if the DER decreases with height within the cloud. Despite the uncertainties in both MODIS and AMSR-E retrievals, the result shows that accounting for the DER vertical variation reduces the mean biases and root-mean-square errors between the MODIS- and AMSR-E-derived LWPs. Besides, the manner in which the DER changes with height has the potential for differentiating precipitative and nonprecipitative warm clouds. For precipitating clouds, the DER at the cloud top is substantially smaller than the DER at the cloud base. For nonprecipitating clouds, however, the DER differences between the cloud top and the cloud base are much less.

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring. Index Terms--Hydrology, meteorology, microwave radiometry, remote sensing, satellite.

Three related algorithms have been developed to retrieve rainfall from the Advanced Microwave Scanning Radiometer--Earth Observing System observations. First, land and ocean backgrounds must be treated separately. The highly reflective ocean background is ideal for observing atmospheric constituents including rainfall. The high and variable emissivity of the land background limits the usefulness of the signal from the liquid hydrometeors under most conditions. For an algorithm with global applicability, it is more reasonable to use the signal generated by scattering of microwaves from frozen hydrometeors. Unfortunately, this scattering is related to rainfall only indirectly. This dichotomy is reinforced by the differing availability of ground truth in the two environments. Thus, two distinct approaches must be used in generating the algorithms for the two cases. The oceanic algorithm is based on physical models, and the land algorithm is generated empirically. Functionally, the two algorithms are merged into a common structure for retrievals on a pixel-by-pixel (Level-2) basis, but the distinct philosophies remain. The third element is the monthly total product (Level-3) which could be generated by simply summing the Level-2 products. However, subtle biases, unimportant on a Level-2 basis, could introduce serious errors into the totals. Therefore, we have chosen to generate a separate product over the oceans consisting of monthly averaged rain rates over 5[degrees] x 5[degrees] boxes. The Level-3 ocean algorithm assumes that the rainfall intensity is distributed log-normally and also absorbs small instrumental calibration errors and some modeling errors. The physical assumptions are, for the most part, a simplification of the assumptions used in the oceanic Level-2 algorithm. The possibilities being more limited, the overland portion of the Level-3 product is generated by simply summing the Level-2 product. Index Terms--Microwave radiometer, rain.

The success of any passive microwave precipitation retrieval algorithm relies on the proper identification of rain areas and the elimination of surface areas that produce a signature similar to that of precipitation. A discussion on the impact of and on methods that identify areas of rain, snow cover, deserts, and semiarid conditions over land, and rain, sea ice, strong surface winds, and clear, calm conditions over ocean. are presented. Additional artifacts caused by coastlines and Special Sensor Microwave/Imager data errors are also discussed, and methods to alleviate their impact are presented. The strengths and weaknesses of the 'screening' techniques are examined through application on various case studies used in the WetNet PIP-2. Finally, a methodology to develop a set of screens for use as a common rainfall indicator for the intercomparison of the wide variety of algorithms submitted to PIP-2 is described.

Rainfall estimates produced from the Special Sensor Microwave/Imager (SSM/I) data have been utilized operationally by the United States Navy since the launch of the first SSM/I sensor in June of 1987. The navy initially contracted Hughes Aircraft Company to develop a rainfall-retrieval algorithm prior to the launch of SSM/I. This first-generation operational navy rainfall retrieval algorithm, referred to as the D-Matrix algorithm, was used until the development of the second-generation algorithm by the SSM/I Calibration/Validation team, which has subsequently been replaced by a third-generation algorithm developed by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information System. Results from both the D-Matrix and Cal/Val algorithms have been included in a total of five algorithm intercomparison projects conducted through the Global Precipitation Climatology Project and WetNet. A comprehensive summary of both quantitative and qualitative results from these intercomparisons is given detailing many of the strengths and weaknesses of the algorithms. based on these results, the D-Matrix algorithm was found to produce excessively large estimates over land and to poorly represent the spatial structure of rainfall systems, especially at higher latitudes. The Cal/Val algorithm produces more realistic structure within storm systems but appears to overestimate the region of precipitation for many systems and significantly underestimates regions of intense rainfall. While the Cal/Val algorithm appears to provide better instantaneous rainfall estimates in the Tropics, the D-Matrix algorithm provides reasonable time-averaged results for monthly or longer periods.

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 [degrees] x 2.5 [degrees] latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino - Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

The Special Sensor Microwave/Imager (SSM/I), first placed into operation in July 1987, has been making measurements of earth-emitted radiation for over eight years. These data are used to estimate both atmospheric and surface hydrological parameters and to generate a time series of global monthly mean products averaged to a 1 [degrees] lat x 1 [degrees] long grid. Specifically, this includes monthly estimates of rainfall and its frequency, cloud liquid water and cloud frequency, water vapor, snow cover frequency, and sea ice frequency. This study uses seasonal mean values to demonstrate the spatial and temporal distributions of these hydrological variables. Examples of interannual variability such as the 1993 flooding in the Mississippi Valley and the 1992-93 snow cover changes over the United States are used to demonstrate the utility of these data for regional applications.