Advanced Technology Microwave Sounder Research Articles

New generation of U.S. satellite microwave sounder achieves high radiometric stability performance for reliable climate change detection.

Observations from the satellite microwave sounders play a vital role in measuring the long-term temperature trends for climate change monitoring. Changes in diurnal sampling over time and calibration drift have been the main sources of uncertainties in the satellite-measured temperature trends. We examine observations from the first of a series of U.S. new generation of microwave sounder, the Advanced Technology Microwave Sounder (ATMS), which has been flying onboard the National Oceanic and Atmospheric Administration (NOAA)/NASA Suomi National Polar-orbiting Partnership (SNPP) environmental satellite since late 2011. The SNPP satellite has a stable afternoon orbit that has close to the same local observation time as NASA's Aqua satellite that has been carrying the heritage microwave sounder, the Advanced Microwave Sounding Unit-A (AMSU-A), from 2002 until the present. The similar overpass timing naturally removes most of their diurnal differences. In addition, direct comparison of temperature anomalies between the two instruments shows little or no relative calibration drift for most channels. Our results suggest that both SNPP/ATMS and Aqua/AMSU-A instruments have achieved absolute stability in the measured atmospheric temperatures within 0.04 K per decade. This uncertainty is small enough to allow reliable detection of the temperature climate trends and help to resolve debate on relevant issues. We also analyze AMSU-A observations onboard the European MetOp-A satellite that has a stable morning orbit 8 hours apart from the SNPP overpass time. Their comparison reveals large asymmetric trends between day and night in the lower- and mid-tropospheric temperatures over land. This information could help to improve climate data records for temperature trend detection with improved accuracy. The SNPP satellite will be followed by four NOAA operational Joint Polar Satellite System (JPSS) satellites, providing accurate and stable measurement for decades to come. The primary mission of JPSS is for weather forecasting. Now, with the added feature of stable orbits, JPSS observations can also be used to monitor changes in climate with much lower uncertainty than the previous generation of NOAA operational satellites.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments: Impacts of Optimal Channel Selection on Retrievals

Accurate information on atmospheric temperature of tropical cyclones (TCs) is important for monitoring and prediction of their developments and evolution. For hurricanes, temperature anomaly in the upper troposphere can be derived from Advanced Microwave Sounding Unit (AMSU) and Advanced Technology Microwave Sounder (ATMS) through either regression-based or variational retrieval algorithms. This study investigates the dependency of TC warm core structure on emission and scattering processes in the forward operator used for radiance computations in temperature retrievals. In particular, the precipitation scattering at ATMS high-frequency channels can significantly change the retrieval outcomes. The simulation results in this study reveal that the brightness temperatures at 183 GHz could be depressed by 30–50 K under cloud ice water path of 1.5 mm, and thus, the temperature structure in hurricane atmosphere could be distorted if the ice cloud scattering was inaccurately characterized in the retrieval system. It is found that for Hurricanes Irma, Maria, and Harvey that occurred in 2017, their warm core anomalies retrieved from ATMS temperature sounding channels 4–15 were more reasonable and realistic, compared with the retrievals from all other channel combinations and earlier hurricane simulation results.

Intercalibration of Cross-Track Scanners in GPM Constellation

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced.

MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.

Toward the Operational Weather Forecasting Application of Atmospheric Stability Products Derived From NUCAPS CrIS/ATMS Soundings

Atmospheric soundings from radiosondes are critical for the weather forecasting, particularly for the diagnostic of atmospheric stability conditions that can lead to thunderstorm development. However, radiosonde observations (RAOBs) are temporally and spatially limited throughout the globe, promoting the use of satellite measurements. This paper assesses the applicability to the operational short-term weather forecasting of atmospheric stability indices and parameters (SIPs) derived from thermodynamic profiles retrieved from the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) using the Suomi National Polar-orbiting Partnership (SNPP) Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) radiances. For this purpose, we validated NUCAPS SIPs against SIPs derived from conventional and dedicated/reference RAOBs collocated with NUCAPS retrievals within a maximum radius of 50 km and ±1-h time difference, over midlatitudes (60°N-30°N) and tropics (30°N-30°S). Stability parameters evaluated include total precipitable water (TPW), lifted index, K-index, total-totals index, and Galvez-Davison index. NUCAPS TPW exhibited the highest level of statistical agreement with RAOBs, with the remaining NUCAPS SIPs exhibiting favorable results (linear correlations ranging between 0.65 and 0.85). Case studies over the Texas/Oklahoma region and the Northern Coast of Brazil demonstrate NUCAPS capability of generating reliable fields of atmospheric stability, as well as capturing synoptic-scale convective signatures, not feasible with RAOBs. Considering also the benefit of SNPP NUCAPS hyperspectral-infrared (IR) and microwave soundings available for both regions during critical early afternoon periods, our analysis supports the use of NUCAPS CrIS/ATMS SIPs as complementary nowcasting tools for the analysis of preconvective environments.

Capturing Size and Intensity Changes of Hurricanes Irma and Maria (2017) from Polar-Orbiting Satellite Microwave Radiometers

Abstract A recently refined hurricane warm-core retrieval algorithm was applied to data from multiple polar-orbiting satellites that carry the Advanced Technology Microwave Sounder (ATMS) and the Advanced Microwave Sounding Unit-A (AMSU-A) to examine the diurnal variability of the warm cores of Hurricanes Irma and Maria. These hurricanes occurred during the 2017 hyperactive Atlantic hurricane season. Compared with data gathered by dropsondes within 100–1700 km of Hurricanes Irma and Harvey, the means and standard deviations of the differences between ATMS-derived and dropsonde-measured temperature profiles were less than 0.7 and 1 K, respectively, in the vertical layer between ~180 and 750 hPa. The temporal evolutions of the ATMS-derived and AMSU-A-derived maximum warm-core temperature anomalies followed more closely that of the minimum mean sea level pressure and slightly less closely that of the maximum sustained wind. The radii of the ATMS-derived warm cores at 4 and 6 K compared favorably with the 34- and 50-kt-wind radii, respectively, of Hurricane Irma (1 kt = 0.51 m s−1). The vertical extent of the warm core toward lower levels increased with increasing intensity when Hurricane Irma experienced a strong intensification because of an enhanced latent heat release associated with diabatic processes. The tropical cyclone (TC) inner cores at upper-tropospheric levels (~250 hPa) were characterized by a single-peaked diurnal cycle with a maximum around midnight. This warm-core cycle may be an important element of TC dynamics and may have relevance to TC structural and intensity changes.

Evaluation of Warm‐Core Structure in Reanalysis and Satellite Data Sets Using HS3 Dropsonde Observations: A Case Study of Hurricane Edouard (2014)

AbstractUsing National Aeronautics and Space Administration Hurricane and Severe Storm Sentinel (HS3) dropsonde observations in the eye of Hurricane Edouard (2014), its warm‐core structure in reanalysis/analysis and satellite data sets are evaluated. All of the data sets generally underestimate the warm‐core strength of Edouard. Advanced Microwave Sounding Unit‐A shows the smallest error in the upper troposphere but the largest error in the lower troposphere. Its successor, Advanced Technology Microwave Sounder, significantly improves the representation of low‐level warming while maintaining its performance in the middle and upper troposphere. Two versions of the Global Forecast System analysis at different resolutions and Climate Forecast System Reanalysis version 2 show better performance than the other reanalyses. Modern Era Retrospective Analysis for Research and Application (MERRA)‐2, Japanese 55‐year Reanalysis, and European Centre for Medium‐Range Weather Forecasts Interim Reanalysis could represent the vertical warm‐core structure, but they markedly underestimate the warm‐core strength. MERRA and North American Regional Reanalysis (especially North American Regional Reanalysis) have the worst skills when reproducing warm cores. MERRA‐2 considerably outperforms its predecessor, MERRA. The warm‐core evolution of Edouard in six reanalysis/analysis data sets is diagnosed and compared. Different physical processes such as diabatic heating and vertical and horizontal advection may be responsible for warm‐core generation at different stages during its lifetime. The inaccurate representation of these physical processes in the reanalyses/analyses results in errors in the warm‐core strength and height. It is suggested that vortex relocation/bogusing, assimilation of tropical cyclone (TC) observation data, and model parameterizations are important to adequately represent warm cores in terms of strength and three‐dimensional structure. This study can shed some light on the selection of suitable data sets for estimating the real‐time warm‐core strength or for TC‐related climate studies. The need for better observations of TCs and model improvements is also highlighted.

Retrieval of Barometric Pressure from Satellite Passive Microwave Observations over the Oceans

AbstractSea surface barometric pressure is one of the few key geophysical variables has not been derived from satellite observations. In this study, we investigate the retrieval of sea surface barometric pressure from passive microwave observations. The direct relationship between surface barometric pressure and upward‐emitted brightness temperature observed by a microwave radiometer is deduced and validated. A statistical retrieval algorithm based on the back‐propagation (BP) neural networks is proposed to estimate sea surface barometric pressure using the observations from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi‐National Polar‐Orbiting Operational Environmental Satellite System Preparatory Project (SNPP) satellite. In situ buoy measurements and reanalysis data are used to assess the retrieval performance. Experimental results show that the proposed retrieval algorithm can estimate sea surface barometric pressure over mid‐to‐low latitude areas ( ) with the root mean square errors of less than 2.0, 3.0, and 3.5 hPa for clear‐sky, cloudy, and rainy conditions, respectively. Several tropical cyclone retrieval experiments also show that the proposed retrieval algorithm behaves well under high wind conditions (15 m/s < wind speed < 28 m/s).

Estimation of Hurricane Maximum Wind Speed Using Temperature Anomaly Derived From Advanced Technology Microwave Sounder

Estimation of hurricane intensity is typically relying on aircraft observations and remote sensing data. This letter develops a satellite algorithm to estimate the hurricane surface maximum wind speed. The Advanced Technology Microwave Sounder (ATMS) observations are used to retrieve the atmospheric temperatures. The temperature anomaly field at each pressure level within the hurricane is derived with respect to its environment. It is shown that for a tropical cyclone entering the hurricane stage, a warm core anomaly occurs in upper troposphere. From the GPS dropsonde data of three hurricane cases (Harvey, Irma, and Maria) in 2017, a linear relationship is derived between the maximum warm core value and maximum surface wind speed. Thus, ATMS-derived warm core anomaly is a very good indicator of storm intensity and can be used in the operational weather forecasts.

Is There Really a Closure Gap Between 183.31-GHz Satellite Passive Microwave and <italic>In Situ</italic> Radiosonde Water Vapor Measurements?

We present a new closure study between radiosonde and microwave satellite humidity measurements. The radiosonde data are from the Global Climate Observing System Reference Upper-Air Network. The satellite data are from the radiometers: MHS, Advanced Technology Microwave Sounder, and Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie. Like previous studies, we find the satellite data to be “colder” than simulated radiosonde data. But the mean bias value (0.4 K) is smaller than previously reported and, according to our analysis, not significant. The error budget suggests an uncertainty of 0.52–1.06 K. We also show that the improvement in closure can be attributed to improvements in the intercomparison methodology.

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.

Assimilation of Feng-Yun-3B satellite microwave humidity sounder data over land

The ECMWF has been assimilating Feng-Yun-3B (FY-3B) satellite microwave humidity sounder (MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz (vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS (Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

Validation of Atmospheric Profile Retrievals From the SNPP NOAA-Unique Combined Atmospheric Processing System. Part 1: Temperature and Moisture

This paper provides an overview of the validation of the operational atmospheric vertical temperature profile (AVTP) and atmospheric vertical moisture profile (AVMP) environmental data record (EDR) products retrieved from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS), two passive sounding systems onboard the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The CrIS/ATMS suite serves as the U.S. low earth orbit (LEO) satellite sounding system and will span the future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm capable of retrieving atmospheric profile EDR products with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The SNPP NUCAPS AVTP and AVMP EDR products are validated using extensive global in situ baseline data sets, namely, radiosonde observations launched from ground-based networks and ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon statistical analyses using these data sets, the SNPP AVTP and AVMP EDRs are determined to meet the JPSS Level 1 global performance requirements.

Assessment of NOAA NUCAPS upper air temperature profiles using COSMIC GPS radio occultation and ARM radiosondes

AbstractThe U.S. National Oceanic and Atmospheric Administration (NOAA) recently began operational processing to derive vertical temperature profiles from two new sensors, Cross‐Track Infrared Sounder and Advanced Technology Microwave Sounder, which were developed for the next generation of U.S. weather satellites. The NOAA‐Unique Combined Atmospheric Processing System (NUCAPS) has been developed by NOAA to routinely process data from future Joint Polar Satellite System operational satellites and the preparatory Suomi‐NPP satellite. This paper assesses the NUCAPS vertical temperature profile product from the upper troposphere into the middle stratosphere using radiosonde and GPS radio occultation (RO) data. Radiosonde data from the Department of Energy Atmospheric Radiation Measurement (ARM) program are=] compared to both the NUCAPS and GPS RO temperature products to evaluate bias and RMS errors. At all three fixed ARM sites for time periods investigated the NUCAPS temperature in the 100–40 hPa range is found to have an average bias to the radiosondes of less than 0.45 K and an RMS error of less than 1 K when temperature averaging kernels are applied. At a 95% confidence level, the radiosondes and RO were found to agree within 0.4 K at the North Slope of Alaska site and within 0.83 K at Southern Great Plains and Tropical Western Pacific. The GPS RO‐derived dry temperatures, obtained from the University Corporation for Atmospheric Research Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, are used as a common reference for the intercomparison of NUCAPS temperature products to similar products produced by NASA from Atmospheric Infrared Sounder (AIRS) and by European Organisation for the Exploitation of Meteorological Satellites from MetOp‐B Infrared Atmospheric Sounding Interferometer (IASI). For seasonal and zonal scales, the NUCAPS agreement with AIRS and IASI is less than 0.5 K after application of averaging kernels.

Application of GCOM-W AMSR2 and S-NPP ATMS Hydrological Products to a Flooding Event in the United States

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.

Mapping coastal floods induced by hurricane storm surge using ATMS data

ABSTRACTThe coastal floods induced by hurricane storm surge are frequent, costly, and deadly hazards. Accurately and quickly estimating the spatial extent of floods is highly important for relief and rescue operations. In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy in late October 2012 using passive microwave remote-sensing data. The approach estimates the water fraction from coarse-resolution Advanced Technology Microwave Sounder (ATMS) data through mixed-pixel linear decomposition. Land and water sample regions generated by river density and land-cover data, the relationship of channels 3, 4, and 16, neighbourhood pixel searching, and the difference of ATMS channels 4 and 3 are all comprehensively taken into account to dynamically determine water and land end members. The difference in the water fraction at the basin scale before and after flooding is calculated to reduce the impacts of soil and vegetation and to avoid pixel-to-pixel errors. Based on the water fraction difference, using the physical characteristics of water inundation that always proceed from the lowest to the highest elevation points in a basin, the flood map derived from the coarse-resolution ATMS measurements was extrapolated to a higher spatial resolution of 100 m using topographic information. Together, these steps represent a water fraction and high-resolution flood (WFHF) mapping process. To evaluate the WFHF mapping methodology presented in this study, the corresponding ground observations (storm-tide sites and high-water-mark data) and the Federal Emergency Management Agency 3 m resolution Hurricane Sandy storm surge flooding (SSF) products are used. The results show that 88% of the storm-tide and high-water-mark sites were located within the WFHF-mapped flood area. There was also good agreement between our WFHF and the SSF areas, with an accuracy of 88% and a correlation of 0.94. Overall, the proposed WFHF methodology was able to produce high-quality and high-resolution flood maps over large-scale coastal areas.

An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan Plateau

Abstract. Using the National Oceanic and Atmospheric Administration's Gridpoint Statistical Interpolation data assimilation system and the National Center for Atmospheric Research's Advanced Research Weather Research and Forecasting (WRF-ARW) regional model, the impact of assimilating Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) satellite data on precipitation prediction over the Tibetan Plateau in July 2015 was evaluated. Four experiments were designed: a control experiment and three data assimilation experiments with different data sets injected: conventional data only, a combination of conventional and ATMS satellite data, and a combination of conventional and CrIS satellite data. The results showed that the monthly mean of precipitation is shifted northward in the simulations and showed an orographic bias described as an overestimation upwind of the mountains and an underestimation in the south of the rain belt. The rain shadow mainly influenced prediction of the quantity of precipitation, although the main rainfall pattern was well simulated. For the first 24 h and last 24 h of accumulated daily precipitation, the model generally overestimated the amount of precipitation, but it was underestimated in the heavy-rainfall periods of 3–5, 13–16, and 22–25 July. The observed water vapor conveyance from the southeastern Tibetan Plateau was larger than in the model simulations, which induced inaccuracies in the forecast of heavy rain on 3–5 July. The data assimilation experiments, particularly the ATMS assimilation, were closer to the observations for the heavy-rainfall process than the control. Overall, based on the experiments in July 2015, the satellite data assimilation improved to some extent the prediction of the precipitation pattern over the Tibetan Plateau, although the simulation of the rain belt without data assimilation shows the regional shifting.

Striping noise reduction for ATMS window channels using a modified destriping algorithm

The Suomi National Polar‐orbiting Partnership Advanced Technology Microwave Sounder (S‐NPP ATMS) upper‐air temperature sounding channels display a clear across‐track striping noise in the numerical weather prediction observed minus background (NWP O–B) fields. The striping magnitude variation is much more significant in ATMS along‐track directions. Such a striping feature is also visible at brightness temperature fields at all 22 channels during an ATMS on‐orbit pitch manoeuvre period. An algorithm was developed earlier by combining a principal component analysis (PCA) and ensemble empirical mode decomposition (EEMD) and worked well for reducing the striping noise at ATMS upper‐air temperature sounding channels. However, at ATMS window channels (channels 1 and 2), artefacts were generated in the destriped observations at places where ATMS scanlines are aligned with either coastline curves or the edges of deep clouds. They are caused by the along‐track sharp gradients of brightness temperatures at window channels when ATMS Earth scenes change from ocean to land or clear‐sky to deep clouds and vice versa. Thus, an additional step is added to the PCA/EEMD method to eliminate such impacts. The modified destriping method performs well for reducing the striping noise at ATMS window channels without introducing the artefacts to the destriped brightness temperature fields.

A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers

AbstractSnowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in‐cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit‐A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar‐orbiting satellites, namely, NOAA‐18, NOAA‐19, Metop‐A, Metop‐B, and Suomi‐NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite‐based statistical model and a set of numerical weather prediction model‐based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one‐dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.

Intercalibration of the GPM Microwave Radiometer Constellation

AbstractThe Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).