Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) ...

/pdf/the-global-precipitation-measurement-mission-4bkud4z4i5.pdf

The Global Precipitation Measurement Mission

The quantification of precipitation on a global scale is critical for applications ranging from climate monitoring to water resource management. Conventional observations through surface gauge networks provide the most direct measure of precipitation, although these are very much limited to land areas, with very few in situ measurements over the oceans. Weather radars, although providing a spatial measure of precipitation, are limited in extent and number. Satellite observations offer an unrivalled vantage point to observe precipitation on a global basis. Since precipitation is spatially and temporally highly variable, satellites are able to provide temporal and spatial samples commensurate with many precipitation characteristics. This paper provides an overall review of global precipitation estimation, providing an outline of conventional measurements, the basis of the satellite systems used in the observation of precipitation, and the generation, availability and validation of the derived precipitation products. Finally, future satellite precipitation missions are presented. Copyright © 2011 Royal Meteorological Society

https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.284

Global precipitation measurement

. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data
records of gridded global sea-ice concentration. These three records are
derived from passive microwave satellite data and offer three distinct
advantages compared to existing records: first, all three records provide
quantitative information on uncertainty and possibly applied filtering at
every grid point and every time step. Second, they are based on dynamic tie
points, which capture the time evolution of surface characteristics of the
ice cover and accommodate potential calibration differences between satellite
missions. Third, they are produced in the context of sustained services
offering committed extension, documentation, traceability, and user support.
The three records differ in the underlying satellite data (SMMR & SSM/I
& SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz
and either 6 or 19 GHz), in their horizontal resolution (25 or 50 km), and
in the time period they cover. We introduce the underlying algorithms and
provide an evaluation. We find that all three records compare well with
independent estimates of sea-ice concentration both in regions with very high
sea-ice concentration and in regions with very low sea-ice concentration. We
hence trust that these records will prove helpful for a better understanding
of the evolution of the Earth's sea-ice cover.

/pdf/version-2-of-the-eumetsat-osi-saf-and-esa-cci-sea-ice-mez6r731k0.pdf

Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

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.

MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System

This article examines the first-guess (FG) departures of microwave imager radiances assimilated in all-sky conditions (i.e. clear, cloudy and precipitating). Agreement between FG and observations is good in clear skies, with error standard deviations around 2 K, but in heavy cloud or precipitation errors increase to 20 K. The forecast model is not good at predicting cloud and precipitation with exactly the right intensity or location. This leads to apparently non-Gaussian behaviour, both heteroscedasticity, i.e. an increase in error with cloud amount, and boundedness, i.e. the size of errors is close to the geophysical range of the observations, which runs from clear to fully cloudy. However, the dependence of FG departure standard deviations on the mean cloud amount is predictable. Using this dependence to normalise the FG departures gives an error distribution that is close to Gaussian. Thus if errors are treated correctly, all-sky observations can be assimilated successfully under the assumption of Gaussianity on which assimilation systems are based. This ‘symmetric’ error model can be used to provide a robust threshold quality-control check and to determine the size of observation errors for all-sky assimilation. In practice, however, this ‘observation’ error is being used to account for the model's difficulty in forecasting cloud, which really comes from errors in the background and in the forecast model. Hence in future it will be necessary to improve the representation of background and model error. Separately, symmetric cloud amount is recommended as a predictor for bias correction schemes, avoiding the sampling problems associated with ‘asymmetric’ predictors like the FG cloud amount. Copyright © 2011 Royal Meteorological Society

Observation errors in all‐sky data assimilation

Geolocation errors in excess of 20-30 km have been observed in the special sensor microwave imager/sounder (F-16 SSMIS) radiometer observations when compared with accurate global shoreline databases. Potential error sources include angular misalignment of the sensor spin axis with the spacecraft zenith, sample timing offsets, nonuniform spin rate, antenna deployment offsets, spacecraft ephemeris, and approximations of the geolocation algorithm in the Ground Data Processing Software. An analysis methodology is presented to automate the process of quantifying the geolocation errors rapidly in terms of partial derivatives of the radiometer data in the along-scan and along-track directions and is applied to the SSMIS data. Angular and time offsets are derived for SSMIS that reveal the root cause(s) of the geolocation errors, while yet unresolved, are systematic, correctable in the ground processing software, and may be reduced to less than 4-5 km (1-sigma).

/pdf/geolocation-error-analysis-of-the-special-sensor-microwave-z0f08tr0pg.pdf

Geolocation Error Analysis of the Special Sensor Microwave Imager/Sounder

The first Special Sensor Microwave Imager/Sounder (SSMIS) was launched in October 2003 aboard the Air Force Defense Meteorological Satellite Program (DMSP) F-16 Spacecraft. As originally conceived, the SSMIS integrates the imaging capabilities of the heritage DMSP conically scanning Special Sensor Microwave/Imager sensor with the cross-track microwave sounders Special Sensor Microwave Temperature and Special Sensor Microwave Humidity Sounder, SSM/T-2 into a single conically scanning 24-channel instrument with extended sounding capability to profile the mesosphere. As such, the SSMIS represents the most complex operational satellite passive microwave imager/sounding sensor flown while, at the same time, offering new and challenging capabilities associated with radiometer channels having common fields of view, uniform polarizations, and fixed spatial resolutions across the active scene scan sector. A comprehensive end-to-end calibration/validation (cal/val) of the first SSMIS initiated shortly after launch was conducted under joint sponsorship by the DMSP and the Navy Space and Warfare Systems Command. Herein, we provide an overview of the SSMIS instrument design, performance characteristics, and major cal/val results. Overall, the first SSMIS instrument exhibits remarkably stable radiometer sensitivities, meeting requirements with considerable margin while providing high-quality imagery for all channels. Two unanticipated radiometer calibration anomalies uncovered during the cal/val-sun intrusion into the warm-load calibration target and antenna reflector emissions-required significant attention during the cal/val program. In particular, the tasks of diagnosing the root cause(s) of these anomalies as well as the development of ground processing software algorithms to mitigate their impact on F-16 SSMIS and hardware fixes on future instruments necessitated the construction of extensive analysis and simulation tools. The lessons learned from the SSMIS cal/val and the associated analysis tools are expected to play an important role in the design and performance evaluation of future passive microwave imaging and sounding instruments as well as guiding the planning and development of future cal/val programs.

/pdf/design-and-evaluation-of-the-first-special-sensor-microwave-1z1pcn7jv8.pdf

Design and Evaluation of the First Special Sensor Microwave Imager/Sounder

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Geolocation Error Analysis of the Special Sensor Microwave Imager/Sounder

Design and Evaluation of the First Special Sensor Microwave Imager/Sounder