Microwave Integrated Retrieval System Research Articles

Warm-Season Microwave Integrated Retrieval System Precipitation Improvement Using Machine Learning Methods

Warm-Season Microwave Integrated Retrieval System Precipitation Improvement Using Machine Learning Methods

The Hunga Tonga‐Hunga Ha'apai Volcanic Eruption as Seen in Satellite Microwave Observations and MiRS Temperature Retrievals

AbstractThe strongest volcanic eruption since the 19th century occurred on 15 January 2022 at Hunga Tonga‐Hunga Ha'apai, generating unprecedented atmospheric waves not seen before in observations. We used satellite microwave observations from (a) Advanced Technology Microwave Sounder (ATMS) on board the National Oceanic and Atmospheric Administration (NOAA)‐20 and the Suomi‐National Polar‐orbiting Partnership (SNPP) and (b) Advanced Microwave Sounding Unit (AMSU)‐A on board Meteorological operational satellite (MetOp)‐B/MetOp‐C to study these waves in the stratosphere immediately after the eruption. The NOAA Microwave Integrated Retrieval System (MiRS) was applied to these microwave observations to produce atmospheric temperature profiles. The atmospheric Lamb wave and fast‐traveling gravity waves are clearly revealed in both the brightness temperatures and the MiRS retrieved temperatures, revealing their vertical phase structures. This study is the first attempt to perform a detailed analysis of the stratospheric impact of the Tonga eruption on operational satellite microwave observations and the corresponding MiRS retrievals.

Use of a U-Net Architecture to Improve Microwave Integrated Retrieval System (MiRS) Precipitation Rates

Use of a U-Net Architecture to Improve Microwave Integrated Retrieval System (MiRS) Precipitation Rates

In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets

AbstractThe National Oceanic and Atmospheric Administration (NOAA) Microwave Integrated Retrieval System (MiRS) algorithm has been producing retrieval products from NOAA‐20 Advanced Technology Microwave Sounder (ATMS) data since shortly after launch in late 2017. Retrieval products from the Suomi National Polar‐orbiting Partnership (SNPP) ATMS data have been similarly available since 2012. Among the products routinely generated is Total Precipitable Water (TPW), based on the vertical integral of the MiRS retrieved water vapor profile. In this study, TPW data from NOAA‐20 ATMS have been the subject of an in‐depth evaluation. The evaluation included characterization of dependence on orbital node (ascending/descending), season, surface type, scan angle, and meridional (zonally averaged) variation. Due to the similarity between NOAA‐20 and SNPP derived TPW, the emphasis here is on the NOAA‐20 evaluation. The globally averaged values of TPW for 2019 are 25.41 and 25.39 mm from NOAA‐20 and SNPP ATMS, respectively, for combined orbits (ascending and descending). For combined orbits, the bias and standard deviation of MiRS NOAA‐20 ATMS TPW with respect to European Centre for Medium‐Range Weather Forecasts/Global Data Assimilation System (ECMWF/GDAS) analyses are 0.79/1.76 mm and 3.65/3.55 mm, respectively, at 10°N where part of annual mean Intertropical Convergence Zone (ITCZ) is located, while at 40°N, corresponding bias and standard deviation values are 0.84/0.67 mm and 3.00/2.55 mm, respectively. The yearly averaged horizontal distribution of MiRS TPW retrieved from NOAA‐20 ATMS is highly correlated with that of ECMWF and GDAS analyses data, with a correlation coefficient of ∼1.

Improvement of MiRS Sea Surface Temperature Retrievals Using a Machine Learning Approach

We report on the development of a machine learning approach to improving sea surface temperature (SST) retrievals based on satellite-based microwave channel measurements at frequencies higher than 23 GHz. The approach uses a deep neural network (DNN) trained using Microwave Integrated Retrieval System physical retrievals as inputs and collocated European Centre for Medium-Range Weather Forecasts analyses for training and validation. The DNN was designed to characterize SST retrieval residual and then used to correct the original retrieval. Evaluation based on one year of independent data showed reduction in retrieval residual standard deviation from 3.22 to 1.80 K in January and 3.02 to 1.92 K in July and reduction in mean residual from 0.30 to 0.08 K in January and 0.61 to 0.22 K in July. Comparisons with multilinear regression and machine learning approaches that used measured brightness temperatures as inputs were significantly less effective in retrieving SST directly, although the DNN used brightness temperature also showed improvements. This indicates that physical retrieval provides valuable information useful in characterizing retrieval residual beyond that of the measured radiances. The DNN approach also effectively removed scan angle dependence of retrieval residuals—an important consideration with cross-track instruments. Sensitivity tests indicated that skill declines with time as time increases from training month, but that skill in the same month, one year later is nearly the same as that of the original training month. This suggests that it may be sufficient to pretrain a stratified model with monthly or seasonal dependence using one full annual cycle, which could then be used in subsequent years with continued good performance.

How Can Microwave Observations at 23.8 GHz Help in Acquiring Water Vapor in the Atmosphere over Land?

Due to concerns about radio frequency interference from emerging telecommunications technology, there have been intensive discussions on the changes to the international Radio Regulations around 24 GHz at the recent World Radiocommunication Conference in 2019. Although the sensitivity to total precipitable water (TPW) at 23.8 GHz over land is small, and in some cases close to zero, state-of-the-art retrieval systems with no dependence on real-time ancillary data (e.g., numerical weather prediction (NWP) model forecasts), such as the National Oceanic and Atmospheric Administration (NOAA) operational Microwave Integrated Retrieval System (MiRS), have been producing reliable TPW products over both ocean and land, which implies that the microwave channel at 23.8 GHz is providing valuable information on TPW over land as well as over ocean. The contradiction between the zero or near-zero sensitivity and practical performance over land raises questions for the remote sensing community and public users of such data. In this study, we examine the underlying physics and include mathematical explanations, which address and clarify the apparent contradiction. The channel at 23.8 GHz is a direct measurement and indispensable for its combined use with microwave temperature and moisture sounding channels.

Preliminary Development and Testing of an EPS-SG Microwave Sounder Proxy Data Generator Using the NOAA Microwave Integrated Retrieval System

The European Organisation for the Exploitation of Meteorological Satellites currently plans to launch Meteorological Operational Satellite Second Generation A1 (Metop-SG A1) with the microwave Sounder (MWS) instrument in 2023. MWS is a cross-track scanning passive microwave radiometer measuring in the range from 23.8 to 229 GHz, which is similar to the Advanced Technology Microwave Sounder (ATMS) on board the satellites in the Joint Polar Satellite System (JPSS) program. To confirm the capability of operational retrieval systems with MWS before its operational deployment, the prerequisites are to have the MWS simulated brightness temperatures and to apply them to a proper testbed. In this study, a preliminary version of MWS proxy data generator has been developed to provide the MWS simulated brightness temperature datasets and the Microwave Integrated Retrieval System (MiRS) is used as a testbed for an operational retrieval system. ATMS simulated brightness temperature datasets are also generated in the same way in which the MWS simulated brightness temperature datasets are generated in order to have a consistent comparison between MWS and ATMS results. MiRS was run with the MWS simulated inputs and the results are comparable to those of the MiRS ATMS experiments. MiRS MWS total precipitable water accuracy and precision are within most JPSS requirements. The change in channel characteristics from ATMS to MWS including the addition of temperature sounding channels at 53.2 and 53.9 GHz and the polarization change in channels 1 and 2 appear to slightly improve performance of MWS over land temperature and water vapor retrievals,respectively.

Comparing the Thermal Structures of Tropical Cyclones Derived From Suomi NPP ATMS and FY-3D Microwave Sounders

Accurate information on the thermal structures of tropical cyclones (TCs) is essential for monitoring and forecasting their intensity and location. In this study, a scene-dependent 1-D variation (SD1DVAR) algorithm is developed to retrieve atmospheric temperature and moisture profiles under all-weather conditions. In SD1DVAR, the background and observation error matrix varies according to the scattering intensity. Especially, the observation error matrix increases in precipitating atmospheres due to a larger uncertainty in the forward operator. With the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite, SD1DVAR can retrieve better thermal structures in the storm life cycle than NOAA Microwave Integrated Retrieval System (MIRS). Comparing with the aircraft dropsonde observations, the temperature and humidity errors from SD1DVAR are about 3 K and 20%, respectively, whereas those from MIRS are around 4 K–5 K and 30%, respectively. SD1DVAR is also applied for Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder (MWHS) onboard FengYun-3D (FY-3D) satellite. The MWTS and MWHS data sets are first combined into a single Comprehensive MicroWave Suite (CMWS) data stream and then used to retrieve the hurricane thermal structures. It is shown that the hurricane structure from CMWS is very similar to that from ATMS. However, due to the availability of 118-GHz measurements from the CMWS, the hurricane temperature vertical structure is better resolved, and the humidity error is also reduced by about 5%.

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products

We evaluate the agreement between automated snow products generated from satellite observations in the microwave bands within NESDIS Microwave Integrated Retrieval System (MIRS) and Microwave Surface and Precipitation Products System (MSPPS), on the one hand, and snow cover maps produced with manual input by the NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS), on the other. MIRS uses physically based retrievals of atmospheric and surface state parameters to provide daily global maps of snow cover and snow water equivalent at 50 km resolution. The older MSPPS delivers daily global maps at the spatial resolution of 45 km and utilizes mostly simple empirical algorithms to retrieve information. IMS daily maps of snow and sea ice cover for the Northern Hemisphere are produced interactively through the analysis of satellite imagery in the visible, infrared, and microwave spectral bands. We compare the performances of these products across the Northern Hemisphere for 2014–2017, using IMS as the standard. In this intercomparison, the daily overall agreement of the automated snow products with IMS ranges between 88% and 99% for MIRS and 87% and 99% for MSPPS. However, daily snow sensitivity is lower, ranging between 36% and 90% for MIRS and 26% and 91% for MSPPS. We analyze this disagreement rate as a function of terrain and land cover type, finding that, relative to IMS, MIRS shows fewer false positives but more false negatives than MSPPS over high elevation and grassland areas.

The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites

This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.

A Framework for Estimating Clear-Sky Atmospheric Total Precipitable Water (TPW) from VIIRS/S-NPP

Atmospheric water vapor content or total precipitable water (TPW) is a highly variable atmospheric constituent, yet it remains one of the meteorological parameters that is most difficult to characterize accurately. We develop a framework for estimating atmospheric TPW from Visible Infrared Imaging Radiometer Suite (VIIRS) data in this study. First, TPW is retrieved from VIIRS top-of-atmosphere (TOA) radiance of channels 15 and 16 using the refined split-window covariance-variance ratio (SWCVR) method. Then, the VIIRS TPW is blended with the microwave integrated retrieval system (MIRS) derived TPW via Bayesian model averaging (BMA) to improve the accuracy of VIIRS TPW. Three years (2014–2017) of ground measurements collected from SuomiNet sites over North America are used to validate the VIIRS TPW and blended TPW. The mean bias error (MBE) and root mean square error (RMSE) of the VIIRS TPW are 0.21 g/cm2 and 0.73 g/cm2, respectively, and the accuracy of the VIIRS TPW in daytime is much better than at night time. The MBE and RMSE of BMA integrated TPW are 0.06 g/cm2 and 0.35 g/cm2, and the accuracy difference between daytime and nighttime is also removed. The global radiosonde measurements are also collected to validate the BMA integrated VIIRS TPW. The MBE and RMSE of the BMA integrated TPW are 0.09 g/cm2 and 0.44 g/cm2 compared to the radiosonde measurements. This accuracy is also superior to the VIIRS TPW. Therefore, it is concluded that the developed framework can be used to derive accurate clear-sky TPW for VIIRS. This is the first time that we can obtain high accuracy TPW from VIIRS. This study will certainly benefit the study of atmospheric processes and climate change.

An Optimal Estimation Retrieval Algorithm for Microwave Humidity Sounding Channels with Minimal Scan Position Bias

AbstractA flexible and physical optimal estimation-based inversion algorithm for retrieving atmospheric water vapor and cloud liquid water path from passive microwave radiometers over the global oceans is presented. The algorithm’s main strength lies in its ability to explicitly account for forward model errors that depend on the Earth incidence angle (EIA) at which a given radiometer measurement is made. Validation of total precipitable water (TPW) retrieved from Microwave Humidity Sounder (MHS) measurements against near-coincident estimates of TPW from SuomiNet GPS ground stations shows that retrieved TPW values agree closely with SuomiNet estimates, and somewhat better than values from the Microwave Integrated Retrieval System that are retrieved from the same MHS instruments. More importantly, it is found that the inclusion of appropriate forward model error assumptions, which are tailored to the EIA and sea surface temperature of the scene being considered, are able to almost entirely eliminate EIA-dependent biases in retrieved TPW. This result holds true across all satellites currently carrying an MHS instrument, despite the fact that only measurements from one satellite are used to estimate forward model errors. The consistency achieved by the retrieval algorithm across all view angles suggests that other inversion algorithms, particularly those for cross-track-scanning radiometers and potential future constellations of small satellites, would benefit from the inclusion of nuanced error assumptions that consider the effect of EIA.

Dynamic Inversion of Global Surface Microwave Emissivity Using a 1DVAR Approach

A variational inversion scheme is used to extract microwave emissivity spectra from brightness temperatures over a multitude of surface types. The scheme is called the Microwave Integrated Retrieval System and has been implemented operationally since 2007 at NOAA. This study focuses on the Advance Microwave Sounding Unit (AMSU)/MHS pair onboard the NOAA-18 platform, but the algorithm is applied routinely to multiple microwave sensors, including the Advanced Technology Microwave Sounder (ATMS) on Suomi-National Polar-orbiting Partnership (SNPP), Special Sensor Microwave Imager/Sounder (SSMI/S) on the Defense Meteorological Satellite Program (DMSP) flight units, as well as to the Global Precipitation Mission (GPM) Microwave Imager (GMI), to name a few. The emissivity spectrum retrieval is entirely based on a physical approach. To optimize the use of information content from the measurements, the emissivity is extracted simultaneously with other parameters impacting the measurements, namely, the vertical profiles of temperature, moisture and cloud, as well as the skin temperature and hydrometeor parameters when rain or ice are present. The final solution is therefore a consistent set of parameters that fit the measured brightness temperatures within the instrument noise level. No ancillary data are needed to perform this dynamic emissivity inversion. By allowing the emissivity to be part of the retrieved state vector, it becomes easy to handle the pixel-to-pixel variation in the emissivity over non-oceanic surfaces. This is particularly important in highly variable surface backgrounds. The retrieved emissivity spectrum by itself is of value (as a wetness index for instance), but it is also post-processed to determine surface geophysical parameters. Among the parameters retrieved from the emissivity using this approach are snow cover, snow water equivalent and effective grain size over snow-covered surfaces, sea-ice concentration and age from ice-covered ocean surfaces and wind speed over ocean surfaces. It could also be used to retrieve soil moisture and vegetation information from land surfaces. Accounting for the surface emissivity in the state vector has the added advantage of allowing an extension of the retrieval of some parameters over non-ocean surfaces. An example shown here relates to extending the total precipitable water over non-ocean surfaces and to a certain extent, the amount of suspended cloud. The study presents the methodology and performance of the emissivity retrieval and highlights a few examples of some of the emissivity-based products.

GPM Products From the Microwave-Integrated Retrieval System

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.

NFLUX Satellite-Based Surface Radiative Heat Fluxes. Part I: Swath-Level Products

AbstractThe Naval Research Laboratory (NRL) ocean surface flux (NFLUX) system originally provided operational near-real-time satellite-based surface state parameter and turbulent heat flux fields over the global ocean. This study extends the NFLUX system to include the production of swath-level shortwave and longwave radiative heat fluxes at the ocean surface. A companion paper presents the production of the satellite-based global gridded radiative heat flux analysis fields. The swath-level radiative heat fluxes are produced using the Rapid Radiative Transfer Model for Global Circulation Models (RRTMG), with the primary inputs of satellite-derived atmospheric temperature and moisture profiles and cloud information retrieved from the Microwave Integrated Retrieval System (MIRS). This study uses MIRS data provided for six polar-orbiting satellite platforms. Additional inputs to the RRTMG include sea surface temperature, aerosol optical depths, atmospheric gas concentrations, ocean surface albedo, and ocean surface emissivity. Swath-level shortwave flux estimates are converted into clearness index values, which are used in data assimilation because the clearness index values are less dependent on the solar zenith angle. The NFLUX swath-level shortwave flux, longwave flux, and clearness index estimates are produced for 1 May 2013–30 April 2014 and validated against observations from research vessel and moored buoy platforms. Each of the flux parameters compares well among the various satellites.

ATMS‐ and AMSU‐A‐derived hurricane warm core structures using a modified retrieval algorithm

AbstractThe Advanced Technology Microwave Sounder (ATMS) is a cross‐track microwave radiometer. Its temperature sounding channels 5–15 can provide measurements of thermal radiation emitted from different layers of the atmosphere. In this study, a traditional Advanced Microwave Sounding Unit‐A (AMSU‐A) temperature retrieval algorithm is modified to remove the scan biases in the temperature retrieval and to include only those ATMS sounding channels that are correlated with the atmospheric temperatures on the pressure level of the retrieval. The warm core structures derived for Hurricane Sandy when it moved from tropics to middle latitudes are examined. It is shown that scan biases that are present in the traditional retrieval are adequately removed using the modified algorithm. In addition, temperature retrievals in the upper troposphere (~250 hPa) obtained by using the modified algorithm have more homogeneous warm core structures and those from the traditional retrieval are affected by small‐scale features from the low troposphere such as precipitation. Based on ATMS observations, Hurricane Sandy's warm core was confined to the upper troposphere during its intensifying stage and when it was located in the tropics but extended to the entire troposphere when it moved into subtropics and middle latitudes and stopped its further intensification. The modified algorithm was also applied to AMSU‐A observation data to retrieve the warm core structures of Hurricane Michael. The retrieved warm core features are more realistic when compared with those from the operational Microwave Integrated Retrieval System (MIRS).

A multisensor, blended, layered water vapor product for weather analysis and forecasting

This paper describes the creation of a near real-time, near-global, four-layer (surface–850, 850–700, 700– 500, and 500–300 hPa) blended layered water vapor (LWV) product using retrieved soundings from five polar-orbiting satellites [National Oceanic and Atmospheric Administration (NOAA)-18 and NOAA-19 satellites; Defense Meteorological Satellite Program F-18 satellite; Meteorological Operational satellite program’s Metop-A satellite; and National Atmospheric and Space Administration (NASA) Aqua satellite]. Both layer precipitable water (LPW) and layer relative humidity (LRH) are included in the blended LWV product. The NASA Atmospheric Infrared Sounder Version 6 retrieval product and NOAA Microwave Integrated Retrieval System soundings are used to create the product, and the use of retrieved water vapor profiles from multiple satellite inputs at different local times allows visualization of the flow of water vapor in layers. The product has advantages and complements geostationary water vapor imagery, derived products from geostationary satellites, and radiosondes in tracking moisture over data sparse regions in cloudy conditions. LPW profiles show absolute values at each layer of the column, while LRH profiles give a sense of whether the column is moistening or drying with height. Examples of the product are given for a severe weather case, the September 2013 Colorado Front Range floods, and a landfalling tropical depression. The usefulness of the product is discussed from the perspective of how tools commonly used by forecasters to analyze water vapor are augmented by the blended layered water vapor fields.

A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS

AbstractWe present in this study the results obtained when applying a physical algorithm based on a variational methodology to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar‐Orbiting Partnership (SNPP) for a consistent retrieval of geophysical data in all weather conditions. The algorithm, which runs operationally at the U.S. National Oceanic and Atmospheric Administration, is applied routinely to a number of sounders from the Polar‐Orbiting Operational Environmental Satellites, the Defense Meteorological Satellite Program, and the European Meteorological Operational satellite constellations. The one‐dimension variational (1DVAR) methodology, which relies on a forward operator, the Community Radiative Transfer Model, allows for solving the inversion of the radiometric measurements into geophysical parameters which have a direct impact on the brightness temperatures. The parameters that are produced by this Microwave Integrated Retrieval System algorithm include the atmospheric temperature T(p), moisture Q(p), and vertically integrated total precipitable water; and the surface skin temperature and emissivity as well as the hydrometeor products of nonprecipitating cloud liquid water and rain‐ and ice‐water paths. In this algorithm, a simple postprocessing is applied to the 1DVAR‐generated emissivity to derive cryospheric products (snow water equivalent and sea‐ice concentration) when the data are measured over these surfaces. The postprocessing is also applied to the hydrometeors products to generate a surface rainfall rate. This comprehensive set of sounding, surface, hydrometeor, and cryospheric products generated from SNPP/ATMS is therefore radiometrically consistent, meaning that when input to the forward operator, it will allow the simulation of the actual brightness temperatures measurements within noise levels. The geophysical consistency between the products, also critical, is satisfied due to the physical approach adopted and the geophysical constraints introduced through the correlation matrix used in the variational system. The results shown in this paper confirm that the performances of all products are within the expected accuracy and precision figures and comparable to performances usually obtained with single‐parameter‐dedicated algorithms, with the added value that the inverted products are both radiometrically and geophysically consistent.

Assessment of a Variational Inversion System for Rainfall Rate Over Land and Water Surfaces

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.