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A few deficiencies remain in the current National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration/Validation System (ICVS) noise calculation method for characterizing noise equivalent differential temperature $(NE\Delta T)$ performance of on-orbit Advanced Microwave Sounding Unit-A (AMSU-A) instruments. This ICVS method only accounts for the noise contribution resulting from one calibration parameter (warm count). The calculation is also dependent upon an assumption that the calibration gain equals to the sensitivity of warm count to temperature. In addition, the dependence of scene temperature is not considered. This study establishes a new methodology by accounting for the noise components resulting from all calibration parameters such as warm counts, warm target temperatures, space view (cold) counts, and their covariance. Each noise component is computed using the product of the overlapping Allan deviation of corresponding parameter and the sensitivity of Earth scene temperature to the parameter. The new method also comprises the variation of scene temperature via scene count in noise estimation. For the AMSU-A instruments aboard the NOAA-18 to NOAA-19 and Metop-A to Metop-C satellites, the magnitudes of the orbital average $NE\Delta T$ calculated using the ICVS method exceed those from the new method by approximately 8%–38%, corresponding to a range from 0.02 to 0.07 K. Particularly, the deviations at the upper temperature sounding channels from 10 to 14 are around 0.05 K. The magnitude of AMSU-A instrument noise can vary by around 18% for sounding channels and 40% for window channels due to scene temperature change. Therefore, the new method demonstrates its significant improvements in characterizing on-orbit AMSU-A instrument noise more accurately and comprehensively.

Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%.

The frequency and spatial distributions of precipitation extremes (PEs) and deep convective clouds (DCC) across the Amazon region were assessed using satellite-derived data. For PEs, CHIRPS dataset for the period 1981–2018 were used to calculate a set of absolute, threshold, duration, and percentile-based threshold indices defined by the Expert Team on Climate Change Detection and Indices. DCC occurrence was assessed based on the Advanced Microwave Sounding Unit data for the period 2002–2018. In northern Amazon (north of $$5^\circ \hbox {S}$$ ) PEs and DCC are more frequent ( $$\ge 60\%$$ frequency) during February–June. Averaged trends over these months have shown increase in daily rainfall above 20 mm of near 3 days over the 1981–2018 period, and an increase of 2 consecutive wet days (P $$\ge 1\,\hbox {mm}$$ ) in the same period. South of $$5^\circ \hbox {S}$$ prevalence of PEs and DCC is largely observed during November–March ( $$\ge 60\%$$ frequency), whereas the longest persistence of dry days is observed during June–August. Though all PE trends point to an intensification of rainfall in November–March, only consecutive dry days in winter (JJA) and spring (SON) show significant trends, pointing to an increase of 7 days over the 38-yr winters. Rainfall extremes over the entire Amazon region were found to be moderate to strongly correlated with the mean vertically integrated moisture divergence, and in southern Amazon also to upper level divergence and upward vertical velocity. Increased frequency of DCC were found over the whole basin ( $$\sim 18\% \,\, \hbox {yr}^{-1}$$ ), in contrast to decreased convective overshooting (up to $$\sim 15.4\% \,\, \hbox {yr}^{-1}$$ ).

We have derived the behavior of decadal temperature trends over the 24 h of local time, based on zonal averages of SABER data, for the years 2012 to 2014, from 20 to 100 km, within 48∘ of the Equator. Similar results have not been available previously. We find that the temperature trends, based on zonal mean measurements at a fixed local time, can be different from those based on measurements made at a different fixed local time. The trends can vary significantly in local time, even from hour to hour. This agrees with some findings based on nighttime lidar measurements. This knowledge is relevant because the large majority of temperature measurements, especially in the stratosphere, are made by instruments on sun-synchronous operational satellites which measure at only one or two fixed local times, for the duration of their missions. In these cases, the zonal mean trends derived from various satellite data are tied to the specific local times at which each instrument samples the data, and the trends are then also biased by the local time. Consequently, care is needed in comparing trends based on various measurements with each other, unless the data are all measured at the same local time. Similar caution is needed when comparing with models, since the zonal means from 3D models reflect averages over both longitude and the 24 h of local time. Consideration is also needed in merging data from various sources to produce generic, continuous, longer-term records. Diurnal variations of temperature themselves, in the form of thermal tides, are well known and are due to absorption of solar radiation. We find that at least part of the reason that temperature trends are different for different local times is that the amplitudes and phases of the tides themselves follow trends over the same time span of the data. Many of the past efforts have focused on the temperature values with local time when merging data from various sources and on the effect of unintended satellite orbital drifts, which result in drifting local times at which the temperatures are measured. However, the effect of local time on trends has not been well researched. We also derive estimates of trends by simulating the drift of local time due to drifting orbits. Our comparisons with results found by others (Advanced Microwave Sounding Unit, AMSU; lidar) are favorable and informative. They may explain, at least in part, the bridge between results based on daytime AMSU data and nighttime lidar measurements. However, these examples do not form a pattern, and more comparisons and study are needed.

Microwave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to the upper stratosphere and lower mesosphere show variabilities due to changes in the AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 data. Furthermore, it explores the use of reprocessed and intercalibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this interplatform sensitivity, which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their intercalibration.

In this article, we first present a generalized methodology for deriving sensor brightness temperature sensor data records (SDR) from antenna temperature data records (TDR) applicable for Advanced Microwave Sounding Unit-A (AMSU-A) instruments. It includes corrections for antenna sidelobe contributions, antenna emission, and radiation perturbation due to the difference of Earth radiance in the main beam and that in the sidelobes that lie outside the main beam but within the Earth disk. For practical purposes, we simplify the methodology by neglecting the components other than the antenna sidelobe contributions to establish a consistent formulation with the legacy AMSU-A antenna pattern correction (APC) formula. The simplified formulation is then applied to the final AMSU-A instrument onboard the Metop-C satellite that was launched in November 2018, in order to compute APC coefficients for deriving SDR from TDR data. Furthermore, the performance of the calculated correction coefficients is validated by calculating the differences between the daily averaged AMSU-A (TDR and SDR) observations against radiative transfer model (O–B) simulations under clear sky conditions, and over open oceans. The validation results show that the derived temperature corrections are channel and scan position dependent, and can add 0.2–1.6 K to the antenna temperatures. In addition, the derived SDR O-B results exhibit a reduced and more symmetric scan angle-dependent bias when compared with corresponding TDR antenna temperatures.

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves vertical profiles of temperature, water vapor, greenhouse and pollutant gases, and cloud properties from measurements made by infrared and microwave instruments on polar-orbiting satellites. These are AIRS/AMSU on Aqua and CrIS/ATMS on Suomi NPP and NOAA20; together they span nearly 2 decades of daily observations (2002 to present) that can help characterize diurnal and seasonal atmospheric processes from different time periods or regions across the globe. While the measurements are consistent, their information content varies due to uncertainty stemming from (i) the observing system (e.g., instrument type and noise, choice of inversion method, algorithmic implementation, and assumptions) and (ii) localized conditions (e.g., presence of clouds, rate of temperature change with pressure, amount of water vapor, and surface type). CLIMCAPS quantifies, propagates, and reports all known sources of uncertainty as thoroughly as possible so that its retrieval products have value in climate science and applications. In this paper we characterize the CLIMCAPS version 2.0 system and diagnose its observing capability (ability to retrieve information accurately and consistently over time and space) for seven atmospheric variables – temperature, H2O, CO, O3, CO2, HNO3, and CH4 – from two satellite platforms, Aqua and NOAA20. We illustrate how CLIMCAPS observing capability varies spatially, from scene to scene, and latitudinally across the globe. We conclude with a discussion of how CLIMCAPS uncertainty metrics can be used in diagnosing its retrievals to promote understanding of the observing system and the atmosphere it measures.

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral instrument with 2378 channels. It is a part of the Aqua space platform equipment. It registers outgoing longwave radiation in the IR-band from 3.74 to 15.4 microns. To correctly retrieve the atmospheric profiles in the presence of cloud structures, AIRS measurement processing scheme uses data from the 15-channel AMSU-A microwave instrument, which is also installed on Aqua. The paper proposes a technology for synthesizing the readings of AMSU-A, that failed in the fall of 2015, by using observations from the 22-channel radiometer of the Advanced Technology Microwave Sounder (ATMS) installed on Suomi-NPP and NOAA-20 satellites. These platforms were launched in 2011 and 2018, respectively. The transition between the coordinate grids of the two instruments was implemented by the “resample” software library, which transferred the radiance temperature values obtained by ATMS radiometer to the AMSU-A irregular measurement grid by means of a Gaussian function. The method was tested for the Aqua and Suomi-NPP neighboring orbits of 2015, when AMSU-A was still operating normally. It is established that the root-mean-square deviation of the radiance temperature values during the transferring of ATMS data to the AMSU-A coordinate grid does not exceed 1%, and the correlation coefficient is 0.98. Using the synthesized AMSU-A readings, reconstructions of the parameters of the atmosphere and the underlying surface were carried out. The analysis of the obtained results showed the suitability of the proposed method of replacing the microwave data from AMSU by the data from ATMS instruments. It should be noted that in the case of a rapidly changing atmosphere, for example, with a strong wind, the use of ATMS observations is possible only if the difference in the passage time of the two satellites does not exceed 10–15 minutes.

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

Microwave temperature sounding observations from polar-orbiting meteorological satellites have been widely used for research on climate trends of atmospheric temperature at different heights around the world. Taking the Amazon rainforest as the target area, this study combined the Microwave Temperature Sounder-2 (MWTS-2) data onboard the Chinese FengYun-3D (FY-3D) satellite with the Advanced Microwave Sounding unit-A (AMSU-A) data onboard the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational (MetOp) polar-orbiting meteorological satellites (i.e., NOAA-15, −18, −19, MetOp-A, -B). The double difference method was used to estimate and thus eliminate the inter-sensor bias, and a decadal diurnal correction was used to reduce the impact of different local equator crossing times on climate trends. The “no-rain” conditions were determined for AMSU-A data by channels 1 and 15, and for MWTS-2 data by channels 1 and 7. Finally, the decadal linear trends of atmospheric temperature from 1998 to 2020 were obtained after applying the inter-sensor bias calibration and inter-decadal diurnal correction to AMSU-A and MWTS-2 data from NOAA-15, −18, −19; MetOp-A, -B; and FY-3D. A warming trend was found in the AMSU-A window and tropospheric channels (1–9 and 15) and a cooling trend in stratospheric channels (10–14). The warming (cooling) trends of channels 7–9 (10) were relatively small. The warming (cooling) trends of AMSU-A channels 1–6 (14–15) were significantly reduced after the inter-decadal diurnal correction.

We have improved a rainfall retrieval algorithm over coast for the Advanced Microwave Sounding Unit (AMSU) in order to upgrade to the Global Satellite Mapping of Precipitation (GSMaP) product version 8. As for the algorithm of product version 7, the zonal-mean monthly precipitation tends to overestimate low latitude coastal areas and to be a missing value the polar side of the winter hemisphere from 40 degrees latitude due to the low temperature screening. In order to improve these problems, we separated the coast into the seaward and landward coasts, and reconsidered Rain/No-Rain classification method, screening conditions and estimation method of each algorithm.

The Community Satellite Processing Package (CSPP) was developed in 2011 with its first release in 2012(http://cimss.ssec.wisc.edu/cspp/history.shtml). Since then, as of January 2021, over 62 new and updated releases of software packages to process the satellite direct broadcast raw data into sensor data records (SDRs) and produce various Environmental Data Records (EDRs). CSPP supports the Direct Broadcast (DB) meteorological and environmental satellite community through the packaging and distribution of open-source science software when possible. CSPP supports DB users of both polar-orbiting and geostationary satellite data processing and regional realtime applications through the distribution of free open source software and training in local product applications. So far comprehensive multiple sensors sensors (AVHRR, MODIS, AIRS, AMSU, VIIRS, CrIS, ATMS, IASI, MHS, ABI, and AHI) onboard multiple international satellite platforms (NOAA-17, NOAA-20, S-NPP, Terra, Aqua, METOP-A/B, GOES 16/17, and Himawari-8) are making global visible, infrared, and microwave imaging and sounding spectral measurements and producing environment products (composite color images, cloud property, aerosol, dust, fire, flood, drought, temperature/water vapor profile, total precipitable water, precipitation, trace gases, ocean/land surface property, and many others).

Satellite microwave temperature sounding data have been widely used in the study of climate trends over the past decades. When merging advanced microwave sounding unit-A (AMSU-A) data from 1998 to 2017 from different satellites, brightness temperature observations can be affected by differences in the center frequency, incidence angle, and local equator crossing time (LECT) among the instruments. Atmospheric drag and gravity variations with latitude can also cause orbital drift that leads to changes in LECT of the same instrument. Inter-sensor calibration and diurnal correction are thus necessary before applying AMSU-A data to climate studies. In this study, AMSU-A data from the National Oceanic and Atmospheric Administration’s (NOAA’s) -15, -18, and -19 satellites and the European Organization for the Exploitation of Meteorological Satellites MetOp-A/-B collected during 1998–2017 were first inter-calibrated by a double difference method to remove inter-sensor biases. AMSU-A data from NOAA-18 was used as the reference for the double difference inter-sensor calibration. A diurnal correction was then applied to data over the Amazon rainforest to eliminate the effects of different LECTs. Finally, linear and nonlinear climate trends were calculated to show that warming (cooling) trends over the Amazon rainforest for window and tropospheric AMSU-A channels 1–8 and 15 (stratospheric channels 9–14) significantly decreased (increased) if inter-sensor calibration and a diurnal correction was applied. The nonlinear climate trends reveal more rapid warming trends for tropospheric sounding channels 3–7 and 15 and less rapid cooling trends for stratospheric channels 10–12 during 1998–2008 than during 2008–2017. Channels 1–2 (channel 13) have cooling (warming) and warming (cooling) trends before and after 2008, respectively.

The total column water vapor (TCWV) over the Tibetan Plateau (TP) is one important indicator of the Asian water tower, and the changes in the TCWV are vital to the climate and ecosystem in downstream regions. However, the observational data is insufficient to understand the changes in the TCWV due to the high elevation of the TP. Satellite and reanalysis data can be used as substitutes, but their quality needs to be evaluated. In this study, based on a homogenized radiosonde data set, a comprehensive evaluation of the TCWV over the TP derived from two satellite data sets (AIRS-only and AIRS/AMSU) and seven existing reanalysis data sets (MERRA, MERRA2, NCEP1, NCEP2, CFSR, ERA-I, JRA55) is performed in the context of the climatology, annual cycle and interannual variability. Both satellite data sets reasonably reproduce the characteristics of the TCWV over the TP. All reanalysis data sets perform well in reproducing the annual mean climatology of the TCWV over the TP (R = 0.99), except for NCEP1 (R = 0.96) and NCEP2 (R = 0.92). ERA-I is more reliable in capturing the spatial pattern of the annual cycle (R = 0.94), while NCEP1 shows the lowest skill (R = 0.72). JRA55 performs best in capturing the features of the interannual coherent variation (EOF1, R = 0.97). The skill-weighted ensemble mean of the reanalysis data performs better than the unweighted ensemble mean and most of the single reanalysis data sets. The evaluation provides essential information on both the strengths and weaknesses of the major satellite and reanalysis data sets in measuring the total column water vapor over the TP.

Observations from the microwave sounding unit (MSU), from 1978 to 2006 and its successor, the Advanced Microwave Sounding Unit-A (AMSU-A, 1998–present), and onboard the National Oceanic and Atmospheric Administration’s polar-orbiting satellites have been widely used for estimating global climate trends. The MSU has a long-term data set, but it is difficult to obtain cloud information like the cloud liquid water path (LWP) directly from this data set. To monitor and investigate the cloud effect on global upper air temperature trends using MSU observations, a cloud detection algorithm must be developed for the MSU. Considering the similar center frequencies of MSU channel 1 and AMSU-A channel 3, a new cloud detection algorithm is established based on the differences between observed and model-simulated brightness temperatures ( $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu $ ( $\phi $ )) of MSU channel 1 or AMSU-A channel 3 over oceans, where $\mu $ ( $\phi$ ) is a latitudinal dependent global mean bias and $\mu $ ( $\alpha$ ) is the global mean bias depending on scan angle. If a data point satisfies the condition of $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu (\phi) \ge 1$ K, it is removed from the clear sky data set. In order to ensure that those points that are partially affected by the clouds, such as clear and cloud mixed fields-of-views located near cloud edges and/or within optically thin clouds, all data points within the 60-km radial distance of the detected point are removed. Validated with the AMSU-A derived LWP retrievals, about 50% of the clear sky data are successively identified and 99% of the cloudy radiances are successively removed for all NOAA-15 AMSU-A data on January 15, April 15, July 15, and October 15, 2002.

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.

The performance of on-orbit satellite microwave instrument noise equivalent differential temperature (NEDT) and Temperature Data Record (TDR) data is always critical for the broad user community in both environmental data record (EDR) product retrieval and numerical weather prediction applications. Based on intensive pre-launch and post-launch calibration studies [1]–[5], this study assesses the post-launch performance of Metop-C AMSU-A instrument noise and antenna temperature data since the launch. It also conducts the quality assessment of the AMSU-A data in comparison with radiative transfer model (RTM) simulations, legacy NOAA-18/19 and Metop-A/B AMSU-A observations, and NOAA-20 ATMS observations. Finally, this study addresses impact of polarization and wavelength inconsistency on AMSU-A antenna temperature differences against ATMS.

A geostationary microwave sounder, capable of providing temperature, water vapor, clouds, precipitation, and wind in the presence of clouds, convection and precipitation, will add tremendously to our ability to observe dynamic atmospheric phenomena, such as hurricanes and severe storms, monsoonal moisture flow, atmospheric rivers, etc. Such a sensor is now feasible, enabled by technology that has been developed under NASA's Instrument Incubator Program at the Jet Propulsion Laboratory. The Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR) is essentially an “AMSU in GEO”, i.e. it has similar capabilities as AMSU would have if it were operating on a geostationary satellite, but with similar spatial resolution. GEO orbits are almost 50 times higher than the LEO orbits that current AMSUs operate from, and the corresponding scaling of aperture size required to maintain spatial resolution has stymied the development of such a sensor for many decades. The aperture synthesis approach implemented with GeoSTAR finally overcomes that obstacle, and the large number of microwave receivers and associated electronics required is made possible with new technology that has now been developed. A low-cost mission operating as a hosted payload on a commercial communications satellite has been designed and is “shovel-ready”. The objectives of such a mission could be to advance our understanding and modeling of storm processes that control rapid or explosive intensification of hurricanes, mesoscale convective systems, and extratropical cyclones, or it could be to support operational weather forecasting and nowcasting. Retrieval of temperature and water vapor profiles is possible even in the presence of moderate precipitation, and with the ability to derive horizontal wind vectors by tracking water vapor features, a rich set of parameters measuring the thermodynamics, dynamics and convection and sampled every 15 minutes, will enable significant progress in storm science and forecasting.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A.

We generated a new Climate Data Record (CDR) of Upper Tropospheric Humidity (UTH) based on observations from the microwave sounders Special Sensor Microwave Temperature - 2 (SSMT-2), Advanced Microwave Sounding Unit - B (AMSU-B) and Microwave Humidity Sounder (MHS). The data record covers the time period between 1994 and 2017 and provides monthly mean 183.31 ± 1 GHz brightness temperatures and derived UTH along with estimates of measurement uncertainty on a 1° × 1° latitude-longitude grid covering the tropical region (30° S to 30° N). For the UTH retrieval we introduce a new definition of UTH. Forgoing the use of the humidity Jacobian as a weighting function, it is easier to apply than the traditional definition without compromising the retrieval accuracy. The same definition can be used to derive UTH from infrared observations, allowing for a more synergistic use of infrared and microwave UTH in the future. The new UTH CDR is validated against an existing UTH data record., Measurement(s) humidity Technology Type(s) Radiometry (Microwave) Factor Type(s) time period • geographic location Sample Characteristic - Environment troposphere • climate system Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12465830

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI–open ocean–atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014–2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements.

In recent years, the study of microwave radiation from the Moon’s surface has been of interest to the astronomy and remote sensing communities. Due to the stable geophysical properties of the Moon’s surface, microwave lunar radiation is highly predictable and can be accurately modeled, given sufficient observations from reliable instruments. Specifically, for microwave remote sensing study, if International System of Unit (SI) traceable observations of the Moon are available, the Moon can thus be used as an SI traceable calibration reference for microwave instruments to evaluate their calibration accuracies and assess their long-term calibration stabilities. Major challenges of using the Moon as a radiometric source standard for microwave sensors include the uncertainties in antenna pattern measurements, the reliability of measurements of brightness temperature (Tb) in the microwave spectrum of the lunar surface, and knowledge of the lunar phase lag because of penetration depths at different detection frequencies. Most microwave-sounding instruments can collect lunar radiation data from space-view observations during so-called lunar intrusion events that usually occur several days each month. Addressed in this work based on Moon observations from the Advanced Technology Microwave Sounder and the Advanced Microwave Sounding Unit/Microwave Humidity Sounder are two major issues in lunar calibration: the lunar surface microwave Tb spectrum and phase lag. The scientific objective of this study is to present our most recent progress on the study of lunar microwave radiation based on satellite observations. Reported here are the lunar microwave Tb spectrum and phase lag from 23 to 183 GHz based on observations of microwave-sounding instruments onboard different satellite platforms. For current Moon microwave radiation research, this study can help toward better understanding lunar microwave radiation features over a wide spectrum range, laying a solid foundation for future lunar microwave calibration efforts.

Heavy precipitation systems that are typical in the Mediterranean area and often devastate the coastal regions, are described and analyzed here by exploiting active and passive microwave measurements and state-of-the-art precipitation products available in the Global Precipitation Measurement (GPM) mission era. The GPM is boosting its key role in integrating the established observational ground-based and satellite-borne tools not only for precipitation monitoring, but also for understanding and characterizing severe weather in the Mediterranean area. In this Chapter, we present three events that have recently challenged observational and forecasting capabilities, and caused damages at the ground. Making use of ground based and satellite-borne instruments, we address the problem of estimating precipitation of a small-scale and short-living intense thunderstorm, the capability to render the 3-D structure of a mesoscale organized convective system, and the key role of spaceborne microwave sensors in the characterization and monitoring of a tropical-like cyclone. To this end, we exploited satellite measurements probably beyond the role they have been designed for, showing few strategies to blend satellite data and products with conventional meteorological data, with the aim to increase the knowledge of severe systems in the Mediterranean area and to support operational forecasting activities in a climate change perspective.

A column EnKF, based on the Canadian global EnKF and using the RTTOV radiative transfer (RT) model, is employed to investigate issues relating to the EnKF assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) radiance measurements. Experiments are performed with large and small ensembles, with and without localization. Three different descriptions of background temperature error are considered: 1) using analytical vertical modes and hypothetical spectra, 2) using the vertical modes and spectrum of a covariance matrix obtained from the global EnKF after 2 weeks of cycling, and 3) using the vertical modes and spectrum of the static background error covariance matrix employed to initiate a global data assimilation cycle. It is found that the EnKF performs well in some of the experiments with background error description 1, and yields modest error reductions with background error description 3. However, the EnKF is virtually unable to reduce the background error (even when using a large ensemble) with background error description 2. To analyze these results, the different background error descriptions are viewed through the prism of the RT model by comparing the trace of the matrix , where is the RT model and is the background error covariance matrix. Indeed, this comparison is found to explain the difference in the results obtained, which relates to the degree to which deep modes are, or are not, present in the different background error covariances. The results suggest that, after 2 weeks of cycling, the global EnKF has virtually eliminated all background error structures that can be “seen” by the AMSU-A radiances.

This study examines the impact of lateral boundary conditions (LBCs) and initial conditions (ICs) in the prediction of land-falling tropical cyclones (TCs) using Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) data assimilation system for the Bay of Bengal (BoB) region. The LBCs are derived from the National Centre for Environmental Prediction (NCEP) Final (FNL) analysis and GFS forecasted datasets at different horizontal and temporal resolutions. The assimilated datasets in the model comprises of NCEP PREBUFR (PILOT, SOUND, SYNOP, METAR, SONDE, BUOY, SHIP and QuikSCAT) and satellite radiances from Advanced Microwave Sounding Unit (AMSU-A, AMSU-B), High Resolution Infra-Red Sounder (HIRS), Microwave Humidity Sounder (MHS) at different assimilation cycles. The impact of the LBCs with varying spatial and temporal resolutions are investigated in detail and the results indicate that the forecasted track during the first 48 h are not affected, however the subsequent forecasted track is significantly influenced by the 3 hourly GFS LBCs. The initial intensity, positional errors, and storm structure show improvements with the assimilation of NCEP PREBUFR and radiance observations at different assimilation cycles. Simulations carried out with improved initial conditions indicate that the forecast (in terms of track, intensity, and trends in the intensification and dissipation) of the cyclonic storm Sidr improved significantly. In addition, five severe land-falling BoB cyclones (Phailin, Lehar, Helen, Madi, and Hudhud) during 2013–2014 were studied using 3 hourly GFS LBCs and with assimilation of NCEP PREBUFR and satellite radiance observations. The results clearly signify the ability of the WRF modeling system in forecasting the track, intensity, and landfall of the storms. The forecasted mean track errors during 2013–2014 cyclones are 65 km, 58 km, 99 km, and 103 km from day-1 to day-4 respectively, while the initial positional errors was about 41 km. The mean landfall time and positional errors are about 3 h and 15 km respectively. The forecasted intensity of the storm in terms of maximum surface wind (MSW) is also reasonably well predicted, and these errors are 4 ms-1, 10 ms-1, 6ms-1, and 8 ms-1 from day-1 to day-4 respectively.

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.

The temperature of Earth’s atmosphere has been monitored continuously since late 1978 by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) flown on polar-orbiting weather satellites. It is well known that these measurements are affected by the scattering and emission from hydrometeors, including cloud water, precipitation, and ice particles. In this study the hydrometeor effects on MSU/AMSU temperature observations are investigated by comparing satellite-observed temperature of the middle troposphere (TMT) with synthetic TMT constructed using temperature fields from ECMWF Interim [ERA-Interim (ERA-I)]. Precipitation data have been used to estimate how much of the difference between these two TMT fields is due to hydrometeor contamination effects. It is shown that there exists a robust linear proportionality between TMT deficit (i.e., the measured TMT minus the synthetic TMT) and precipitation at individual grid points in monthly mean fields. The linear correlation is even stronger in the annual mean and seasonally varying climatology and also in the spatial pattern of ENSO-related anomalies. The linear regression coefficient obtained in all of these analyses is virtually identical: −0.042 K (mm day−1)−1. The channel that senses lower-tropospheric temperature (TLT) is more sensitive to precipitation than the TMT channel: the regression coefficient is −0.059 K (mm day−1)−1. It is shown that correcting the TMT or TLT monthly anomalies by removing the hydrometeor contamination does not significantly influence estimates of tropical mean temperature trends, but it could affect the pattern of temperature trend over the tropical oceans.

Longwave downward radiation (LWDR) is another major energy source received by the earth's surface apart from solar radiation. Its importance in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a large number of efforts have been made to derive LWDR from space under only clear-sky conditions leading to difficulty in utilizing space-based LWDR in most models due to its spatio-temporal discontinuity. Currently, only a few studies are focused on LWDR estimation under cloudy skies, while their global application is still questionable. In this paper, an alternative strategy is proposed aiming to derive high-resolution (1 km) cloudy-sky LWDR by fusing collocated satellite multi-sensor measurements. The results show that the newly developed method works well and can derive LWDR at a better accuracy with RMSE

Measurements of brightness temperatures from Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding instruments onboard NOAA Polarorbiting Operational Environmental Satellites (POES) have been extensively used for studying atmospheric temperature trends over the past several decades. Intersensor biases, orbital drifts and diurnal variations of atmospheric and surface temperatures must be considered before using a merged long-term time series of AMSU-A measurements from NOAA-15, -18, -19 and MetOp-A.We study the impacts of the orbital drift and orbital differences of local equator crossing times (LECTs) on temperature trends derivable from AMSU-A using near-nadir observations from NOAA-15, NOAA-18, NOAA-19, and MetOp-A during 1998–2014 over the Amazon rainforest. The double difference method is firstly applied to estimation of inter-sensor biases between any two satellites during their overlapping time period. The inter-calibrated observations are then used to generate a monthly mean diurnal cycle of brightness temperature for each AMSU-A channel. A diurnal correction is finally applied each channel to obtain AMSU-A data valid at the same local time. Impacts of the inter-sensor bias correction and diurnal correction on the AMSU-A derived long-term atmospheric temperature trends are separately quantified and compared with those derived from original data. It is shown that the orbital drift and differences of LECTamong different POESs induce a large uncertainty in AMSU-A derived long-term warming/cooling trends. After applying an inter-sensor bias correction and a diurnal correction, the warming trends at different local times, which are approximately the same, are smaller by half than the trends derived without applying these corrections.

This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.

This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics.

Global-mean radiances observed by the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit A (AMSU-A) are analyzed from 2003 to 2012. The focus of this study is on channels sensitive to emission and absorption in the stratosphere. Optimal fingerprinting is used to obtain estimates of changes of stratospheric temperature in five vertical layers due to external forcing in the presence of natural variability. Natural variability is estimated using synthetic radiances based on the 500-yr GFDL CM3 and 240-yr HadGEM2-CC control runs. The results show a cooling rate of 0.65 ± 0.11 (2 σ) K decade−1 in the upper stratosphere above 6 hPa, approximately 0.46 ± 0.24 K decade−1 in two midstratospheric layers between 6 and 30 hPa, and 0.39 ± 0.32 K decade−1 in the lower stratosphere (30–60 hPa). The cooling rate in the lowest part of the stratosphere (60–100 hPa) is −0.014 ± 0.22 K decade−1, which is smallest among all five layers and statistically insignificant. The synergistic use of well-calibrated passive infrared and microwave radiances permits disambiguation of trends of carbon dioxide and stratospheric temperature, increases vertical resolution of detected stratospheric temperature trends, and effectively reduces uncertainties of estimated temperature trends.

Temperature changes in the lower and middle stratosphere during 2001-2016 are evaluated using measurements from GPS Radio Occultation (RO) and Advanced Microwave Sounding Unit (AMSU) aboard the Aqua satellite. After downsampling of GPS-RO profiles according to the AMSU weighting functions, the spatially and seasonally resolved trends from the two data sets are in excellent agreement. The observations indicate that the middle stratosphere has cooled in the time period 2002-2016 at an average rate of –0.14±0.12 to –0.36±0.14 K/decade, while no significant change was found in the lower stratosphere. The meridionally and vertically resolved trends from high-resolution GPS-RO data exhibit a marked inter-hemispheric asymmetry and highlight a distinct boundary between tropospheric and stratospheric temperature change regimes matching the tropical thermal tropopause. The seasonal pattern of trend reveals significant opposite-sign structures at high and low latitudes, providing indication of seasonally varying change in stratospheric circulation.

On July 8, 2014 meteorological satellite “Meteor-M” no. 2 with a module of temperature and humidity atmospheric sensing MTVZA-GY was launched on a circular sun-synchronous orbit. Microwave radiometer MTVZA-GY has 16 imaging channels at frequencies of 10.65, 18.7, 23.8, 31.5, 36.5, 42.0, 48.0, and 91.65 GHz and 13 sounder channels at frequencies in the ranges of 52–57 and 176–191 GHz and carries out conical scanning of the Earth at the angle of incidence 65°. Swath width is 1500 km. The brightness temperatures TBs were computed by numerical integration of microwave radiative transfer equation using the radiosonde and reanalysis atmospheric profiles as the input information. The TBs for the Amazon forest and the calm Ocean under clear sky served for the vicarious calibration of MTVZA channels. The long-term stability of MTVZA-GY operation was examined by the analysis of time series of TBs measured over the test areas around the Eastern Antarctic Dome C station, Greenland GEOSummit station and Amazon rainforest. TBs time series obtained by the GCOM-W1 AMSR2 radiometer for the same test areas served as a reference and were used for a comparison with MTVZA-GY time series. Examples are given of the global MTVZA-GY measurements.

This paper discusses the radiometric calibration of millimeter sounder radiometers, on polar orbiter satellites in the NASA Global Precipitation Mission (GPM) constellation; and presents radiometric bias results. Because the Tropical Rainfall Measurement Mission (TRMM) operated for over 17 years, it is important to combine the TRMM and the GPM precipitation datasets to produce a climate data record for global climate change studies. In the last decade of TRMM's operation, sounder radiometers were introduced into the TRMM constellation, which included: Advanced Microwave Sounding Unit-B sensors flown on NOAA weather satellites and the microwave humidity sounders sensors flown on NOAA and meteorological operational satellite program satellites. These sensors have provided an invaluable dataset of radiance measurements with full earth coverage, which has been used in precipitation measurements, weather prediction, and climate studies.

Two new constellations comprising 14 small satellites with microwave instruments onboard are proposed in this study. Properly arranged, the first constellation is capable of covering the entire globe at an hourly interval and the second constellation is more favorable for the tropical area. Compared to the current JPSS and MetOp satellite constellation, which has passive microwave sounding instruments ATMS or AMSU, a small satellite constellation is more cost effective, requires a shorter development cycle, and has a smaller launch-failure impact. For a designated microwave small satellite constellation, the brightness temperature distribution in space and time is simulated using the operational forecast fields as inputs to the Community Radiative Transfer Model (CRTM). It is demonstrated that the structural change of fast-evolving weather systems such as a middle-latitude cyclone can be well captured from small satellite brightness temperatures.

In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near-nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution.

Version 6 of the UAH MSU/AMSU global satellite temperature dataset represents an extensive revision of the procedures employed in previous versions of the UAH datasets. The two most significant results from an end-user perspective are (1) a decrease in the global-average lower tropospheric temperature (LT) trend from +0.14°C decade−1 to +0.11°C decade−1 (Jan. 1979 through Dec. 2015); and (2) the geographic distribution of the LT trends, including higher spatial resolution, owing to a new method for computing LT. We describe the major changes in processing strategy, including a new method for monthly gridpoint averaging which uses all of the footprint data yet eliminates the need for limb correction; a new multi-channel (rather than multi-angle) method for computing the lower tropospheric (LT) temperature product which requires an additional tropopause (TP) channel to be used; and a new empirical method for diurnal drift correction. We show results for LT, the midtroposphere (MT, from MSU2/AMSU5), and lower stratosphere (LS, from MSU4/AMSU9). A 0.03°C decade−1 reduction in the global LT trend from the Version 5.6 product is partly due to lesser sensitivity of the new LT to land surface skin temperature (est. 0.01°C decade−1), with the remainder of the reduction (0.02°C decade−1) due to the new diurnal drift adjustment, the more robust method of LT calculation, and other changes in processing procedures.

Spencer and Christy of the University of Alabama in Huntsville (UAH) recently introduced a new method to process MSU/AMSU satellite brightness temperature data with their version 6 (v6) data. A comparison of UAH v6 north polar lower stratospheric (TLS) data with that from Remote Sensing Systems (RSS) is presented, indicating a possible bias between 1986 and 1988. Comparing UAH and NOAA Center for Satellite Applications and Research (NOAA) TLS data produces a similar result. An additional analysis utilizing midtropospheric (TMT) data also found a similar bias. Comparing the NOAA TMT data for the May 2016 release against UAH and RSS TMT evidenced another excursion, dated at the middle of 2005, that was corrected in later releases. These comparisons reinforce the concerns expressed by other analysts regarding the merging procedure for UAH v6, repeating similar concerns regarding the earlier UAH v5 products. Any biases in the UAH, RSS, or STAR products would impact the trends calculated for these products and could explain the differences between these trends. Biases in the UAH series would also impact the UAH TLTv6 lower-troposphere product, which is a linear combination of the UAH TMT, tropopause temperature (TTP), and TLS series.

It is proposed and substantiated that, in the ocean‒atmosphere system, the microwave self-radiation intensity (brightness temperature), which is measured by means of satellites in the millimeter and centimeter wave bands, can be used as the direct characteristic of the thermal and dynamic parameters of the system. The vertical turbulent fluxes of sensible and latent heat and momentum are determined at the ocean‒atmosphere interface according to the data measured by the special sensor microwave imagers located on weather satellites created in the scope of the defense meteorological satellite program and the advanced microwave scanning radiometer installed aboard the EOS Aqua oceanographic satellite. For the first time, the integrated (annual) characteristics of heat exchange between the ocean and atmosphere are investigated in the energy-active zones of the North Atlantic with the help of long-term satellite microwave radiometric measurements.