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Satellite remotely sensed products provide critical information to weather forecasters at the National Oceanic and Atmospheric Administration (NOAA) and greatly supplement spare or nonexistent surface observations. Low earth orbiting passive microwave sensors provide unique information related to atmospheric moisture and precipitation, as well as surface properties such as oceanic wind speed. This paper will focus on two microwave sensors that are part of NOAA's Joint Polar Satellite System—the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Technology Microwave Sounder (ATMS)—which are flown on Japan's Global Change Observation Mission—Water (GCOM-W1) and the Suomi National Polar orbiting Partnership (S-NPP) satellites. The orbits of GCOM-W1 and S-NPP are such that AMSR2 and ATMS observe the same region of the earth nearly the same time of the day. This allows for comparison of similar products from the two sensors, which is of great interest to users such as NOAA's National Weather Service. In this paper, we focus on product comparisons for a historic flooding event in the U.S. associated with Hurricane Joaquin. The microwave products are presented and shown with other widely used in-situ observations. The performance of AMSR2 and ATMS products are compared. The higher spatial resolution of the AMSR2 sensor provides more detailed information on water vapor and rain rate compared to ATMS. On the other hand, the wider swath width of the ATMS provides a more continuous field of water vapor and precipitation, as well as water vapor retrievals over land. [ABSTRACT FROM PUBLISHER]

An updated version of the microwave-integrated retrieval system (MiRS) V11.2 was recently released. In addition to the previous capability to process multiple satellites/sensors, the new version has been extended to process global precipitation measurement (GPM) microwave imager (GMI) measurements. The main purpose of this study is to introduce MiRS GPM products and to evaluate rain rate, total precipitable water (TPW), and snow water equivalent (SWE) using various independent datasets. Rain rate evaluations were performed for January, April, July, and October 2015 which represents one full month in each season. TPW was evaluated on four days: 9 January, 1 April, 13 July, and 1 October, which represents one full day in each season. SWE was evaluated for a week in January 2015. Results show that MiRS performance is generally satisfactory in regards to both global/regional geographical distribution and quantified statistical/categorical scores. Histograms show that MiRS GPM rain rate estimates have the capability to reproduce moderate to heavy rain frequency distribution over land, and light rain distribution over ocean when compared with a ground-based reference. Evaluations of TPW show the best performance over ocean with the correlation coefficient, bias, and standard deviation of 0.99, <1.25 mm, and <2.4 mm, respectively. Robust statistical results were also obtained for SWE, with a correlation coefficient, bias, and standard deviation of 0.77, 1.72 cm, and 3.61 cm, respectively. The examples shown demonstrate that MiRS, now extended to GPM/GMI, is capable of producing realistic retrieval products that can be used in broad applications including extreme weather events monitoring, depiction of global rainfall distribution, and water vapor patterns, as well as snow cover monitoring. [ABSTRACT FROM PUBLISHER]

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products. [ABSTRACT FROM AUTHOR]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]