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Reanalysis data are extensively employed in investigation of the tropical large-scale climate variability. In this study, we attempt to use the Advanced Microwave Sounding unit-A (AMSU-A) window channel brightness temperature (TB) observations to examine their relationship with the sea surface temperature (SST). A close relationship is found between the AMSU-A window channel TB and SST in the tropical oceans. A TB Niño Index (TNI) of AMSU-A window (sounding) channels is found to sometimes reach its maximum for about 2 months ahead of Oceanic Niño Index (ONI) for half of El Niño cases from 1998 to 2020. The AMSU-A sounding channels TNI had minimum values about 1–3 months behind ONI peak month. Spatial correlation maps of AMSU-A window channel TB anomalies with TNI are similar to those of SST anomalies with ONI, featuring a horseshoe-shaped pattern in the tropical Pacific and a dipole-shaped pattern in the tropical Indian Ocean. The AMSU-A retrieved liquid water path (LWP) is also highly correlated with the ONI, with larger values in El Niño years. An active development of tropical convective disturbances and their movement associated with westerly wind bursts may be the reason for the peak TNI to occur 1–3 months earlier than the peak ONI. The potential for AMSU-A TB observations to reveal El Niño signals adds additional satellite data source for El Niño research. [ABSTRACT FROM AUTHOR]

A unique End-of-Life (EOL) Deep Space View Test (DSVT) was performed on 27 November 2021 for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard the first EUMETSAT MetOp-A satellite in the deorbiting process. The purpose of this test is to recalibrate the antenna sidelobe, to derive antenna emission, and to quantify the in-orbit asymmetric scan biases of AMSU-A and MHS to, ultimately, improve Near Real-Time (NRT) products for MetOp-B and -C and the entire Fundamental Climate Data Records (FCDR). In this study, MetOp-A AMSU-A and MHS EOL DSVT data on 27 November 2021 have been analyzed. The deep space scene antenna temperatures were first applied for the antenna pattern correction; then, the antenna reflector channel emissivity values were derived from the corrected temperatures. For the MHS, the observed scan-angle-dependent brightness temperatures (BTs) for all channels were well behaved after the antenna pattern correction, except for channel 1. The derived antenna reflector emissivity values from this test are 0.0016, 0.0036, 0.0036, and 0.0019 for channels 1, 3, 4, and 5, respectively. For AMSU-A, the deep space view counts were not homogeneous during the test period, exhibiting large variations in the along-track and cross-track directions, mainly due to the instrument temperature's rapid change during the test period. The large relative noise in the deep space view observations negatively impacted the data quality and limits the value of this test. The large relative noise may contribute to the different emissivity values derived from the same frequency for channels 9 to 14. We also found unexpected scan-angle-dependent BT after antenna pattern correction for quasi-vertical (QV) channels 1 and 2 when compared to the emission model. Further investigation using a simulation confirmed that channels 1 and 2 are QV channels, as designed. [ABSTRACT FROM AUTHOR]

This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument—scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1—has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A/MHS model evaluation. The results clearly show the NN algorithm's high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A/MHS)-simulated dataset, respectively. [ABSTRACT FROM AUTHOR]

A unique End-of-Life (EOL) Deep Space View Test (DSVT) was performed on 27 November 2021 for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard the first EUMETSAT MetOp-A satellite in the deorbiting process. The purpose of this test is to recalibrate the antenna sidelobe, to derive antenna emission, and to quantify the in-orbit asymmetric scan biases of AMSU-A and MHS to, ultimately, improve Near Real-Time (NRT) products for MetOp-B and -C and the entire Fundamental Climate Data Records (FCDR). In this study, MetOp-A AMSU-A and MHS EOL DSVT data on 27 November 2021 have been analyzed. The deep space scene antenna temperatures were first applied for the antenna pattern correction; then, the antenna reflector channel emissivity values were derived from the corrected temperatures. For the MHS, the observed scan-angle-dependent brightness temperatures (BTs) for all channels were well behaved after the antenna pattern correction, except for channel 1. The derived antenna reflector emissivity values from this test are 0.0016, 0.0036, 0.0036, and 0.0019 for channels 1, 3, 4, and 5, respectively. For AMSU-A, the deep space view counts were not homogeneous during the test period, exhibiting large variations in the along-track and cross-track directions, mainly due to the instrument temperature’s rapid change during the test period. The large relative noise in the deep space view observations negatively impacted the data quality and limits the value of this test. The large relative noise may contribute to the different emissivity values derived from the same frequency for channels 9 to 14. We also found unexpected scan-angle-dependent BT after antenna pattern correction for quasi-vertical (QV) channels 1 and 2 when compared to the emission model. Further investigation using a simulation confirmed that channels 1 and 2 are QV channels, as designed.

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6. [ABSTRACT FROM AUTHOR]

This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument—scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1—has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A/MHS model evaluation. The results clearly show the NN algorithm’s high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A/MHS)-simulated dataset, respectively.

The northeast China cold vortex (NCCV) refers to a closed low‐pressure system over northeast China extending from China‐Siberia to the northwest Pacific coast region. It is cut off from the westerly jet belt in the middle and upper troposphere, has a cold core, lasts several days, and could bring heavy rain events in late spring and summer that are of great challenge to weather forecasters. This study investigates the possibility of observing temperature structures of NCCVs by two microwave temperature sounders, that is, the Microwave Temperature Sounder‐2 (MWTS‐2) and the Advanced Microwave Sounding Unit‐A (AMSU‐A) onboard the two polar‐orbiting operational environmental satellites (POES) Fenyun‐3 satellite FY‐3D and Meteorological Operational satellite MetOp‐B, respectively. The MWTS‐2 and AMSU‐A together provide 3D atmospheric temperatures in the middle and upper troposphere, as well as the lower stratosphere four times daily. Both limb‐corrected direct observations of brightness temperature and retrievals of atmospheric temperature within NCCVs are characterized by cold and warm cores below and above the tropopause, respectively, comparing favorably with the European Centre for Medium‐Range Weather Forecasts ERA5 reanalysis. Compared with the ERA5 reanalysis in June 2019, the MWTS‐2 retrieved atmospheric temperatures have no scan‐dependent biases, the biases are smaller than ±0.2K, and root‐mean‐square errors (RMSEs) are smaller than and 2.0K at all altitudes. This study suggests the possibility of using multiple POES microwave‐temperature‐sounding observations for near‐real‐time monitoring of NCCVs' 3D temperature structures. Plain Language Summary: Near‐real‐time monitoring of the northeast China cold vortex (NCCV)'s 3D temperature structures by the polar‐orbiting satellite microwave observations. Both limb‐corrected direct observations of brightness temperature and temperature retrievals within NCCVs are characterized by cold and warm cores below and above the tropopause, respectively, comparing favorably with ERA5 reanalysis. Key Points: This study investigates the possibility of observing temperature structures of NCCVs by two microwave temperature sounders MWTS‐2 and AMSU‐A [ABSTRACT FROM AUTHOR]

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6.

Abstract Surface emissivity has a significant impact on atmospheric parameter retrievals from microwave sounding instruments. To reduce the dependence of retrievals on surface emissivity, a double channel differences equation is deduced, and a corresponding retrieval scheme is constructed. Retrieval experiments are performed using Advanced Microwave Sounding Unit‐A (AMSU‐A) and Microwave Humidity Sounder (MHS) simulations and global measurements. Simulation experiments show that the double channel differences scheme can reduce the root mean square errors (RMSE) of the temperature and humidity profiles in the middle and lower atmosphere. Retrieval experiments based on AMSU‐A and MHS global measurements show that the proposed scheme can significantly reduce the RMSE of temperature profiles in the lower atmosphere and humidity profiles in the middle and lower atmosphere for cloudy and cloudless conditions, different surface types, and different scan angles, with maximum reduction values of 0.64 K and 9.03%, respectively. Regarding RMSE improvement, that of the cloudy condition is greater than that of the cloudless condition, that of the land is greater than that of the coast and the sea, and there is no significant dependence on the scan angles. The double channel differences scheme is very sensitive to initial near‐surface temperatures. Reducing the initial near‐surface temperature error can significantly improve the temperature retrieval accuracy below 900 hPa, with maximum reduction value of 3.25 K.

Surface emissivity has a significant impact on atmospheric parameter retrievals from microwave sounding instruments. To reduce the dependence of retrievals on surface emissivity, a double channel differences equation is deduced, and a corresponding retrieval scheme is constructed. Retrieval experiments are performed using Advanced Microwave Sounding Unit‐A (AMSU‐A) and Microwave Humidity Sounder (MHS) simulations and global measurements. Simulation experiments show that the double channel differences scheme can reduce the root mean square errors (RMSE) of the temperature and humidity profiles in the middle and lower atmosphere. Retrieval experiments based on AMSU‐A and MHS global measurements show that the proposed scheme can significantly reduce the RMSE of temperature profiles in the lower atmosphere and humidity profiles in the middle and lower atmosphere for cloudy and cloudless conditions, different surface types, and different scan angles, with maximum reduction values of 0.64 K and 9.03%, respectively. Regarding RMSE improvement, that of the cloudy condition is greater than that of the cloudless condition, that of the land is greater than that of the coast and the sea, and there is no significant dependence on the scan angles. The double channel differences scheme is very sensitive to initial near‐surface temperatures. Reducing the initial near‐surface temperature error can significantly improve the temperature retrieval accuracy below 900 hPa, with maximum reduction value of 3.25 K. Plain language summary: Microwave sounding instruments such as AMSU‐A/MHS provide an unique ability to acquire global atmospheric temperature and humidity profiles. However, at present, their ability to acquire temperature and humidity information in the lower atmosphere has not been fully exploited, mainly due to the impact of surface emissivity. Therefore, reducing the impact of surface emissivity is a key issue. In this paper, a temperature and humidity profiles retrieval scheme is constructed by using the relationship between the surface emissivity of adjacent channels. The research shows that the scheme can significantly improve temperature and humidity retrieval accuracy for cloudy and cloudless conditions, different surfaces types, different scan angles. Reducing initial values error in the scheme can further improve retrieval accuracy. Key Points: We propose a microwave temperature and humidity retrieval scheme based on double channel differencesWe show the accuracy performance of the double channel differences retrieval scheme for AMSU‐A/MHS measurementsResults demonstrate a significant retrieval improvement over a traditional AMSU‐A/MHS neural network retrieval scheme [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Sounder (AIRS) Obs4MIPs (Observations for Model Intercomparison Projects) Version 2 data set includes monthly mean tropospheric air temperature, specific humidity, and relative humidity for each calendar month from September 2002 to September 2016, on a global 1° × 1° latitude‐longitude spatial grid and on the Coupled Model Intercomparison Project eight mandatory vertical pressure levels from 1,000 to 300 hPa. It also includes standard error and number of observations, for an estimate of AIRS data retrieval error and sampling uncertainty. Three new technical notes describe the data set. This data set is designed for Coupled Model Intercomparison Project climate model evaluation and has been published at Earth System Grid Federation data centers since April 2018. It adds new monthly mean tropospheric relative humidity data to Obs4MIPs and updates and extends the monthly mean tropospheric air temperature and specific humidity data in AIRS Obs4MIPs Version 1 data set. The data source for this data set is the AIRS Version 6 Level 3 standard monthly mean air temperature, specific humidity, and relative humidity data products in the "TqJoint" grids from the AIRS and Advanced Microwave Sounding Unit A combined physical retrievals. This paper documents this data set in terms of motivation for this data set, data description, data origin and processing procedures, major improvements from the previous version, and caveats for its use in climate model evaluation. Plain Language Summary: We have published a new data set designed for climate model evaluation from the infrared and microwave atmospheric sounding system (Atmospheric Infrared Sounder and Advanced Microwave Sounding Unit A) on the NASA Aqua satellite. In this paper, we briefly describe the reasons for providing this data set, its detailed data structure, the original data and processing procedures used to generate this data set, major improvements in comparison to the previous version, and the issues to be considered for climate model evaluation with it. Key Points: A new data set containing Atmospheric Infrared Sounder observations and designed for climate model evaluation has been publishedThis data set includes monthly mean gridded tropospheric air temperature, specific humidity, and relative humidity at eight pressure levelsThe standard error and number of observations, for an estimate of data uncertainty, along with three technical notes are also provided [ABSTRACT FROM AUTHOR]

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.

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.

The Advanced Microwave Sounding Unit (AMSU)-A/Advanced Technology Microwave Sounder (ATMS) onboard the National Oceanic Atmospheric Administration (NOAA)-18/-19, MetOp-A/-B, and Suomi National Polar-orbiting Partnership satellites provide global observations of the cloud Liquid Water Path (LWP) almost 10 times a day. This study explores the possibility of capturing the diurnal cycle of the LWP. An inter-satellite cross-calibration is first carried out using a double-difference method. A remapping is then used to obtain the AMSU-A-like LWP to account for beam shape discrepancies between the ATMS and AMSU-A. We finally examine the diurnal cycle of the LWP over the Southeast Pacific Ocean using the ATMS and AMSU-A data from the five satellites mentioned above. Results show that the remapped ATMS results agree well with the AMSU-A results at the same local time over a stratocumulus region. LWP retrievals from multiple satellite cross-track microwave radiometers can well reproduce the diurnal variation characteristics of LWP in 2015 over the East Pacific Ocean, including the seasonal variation of the diurnal variation. This study presents the first step toward merging LWP data from all ATMS and AMSU-A radiometers and will be of interest to many researchers studying LWP-related weather and climate changes, especially considering the possible loss of higher-resolution microwave-frequency conical-scanning sensors in the coming years.

As medidas do AMSU-A para os canais que são sensíveis à superfície terrestre sobre os continentes não tem sido amplamente utilizadas para ajustar a previsão numérica de tempo de curto prazo (PNTs), devido à complexidade das características da superfície terrestre. Nesse sentido, o presente artigo utiliza o Sistema de Assimilação de Dados (G3DVAR) do Centro de Previsão de Tempo e Estudos Climáticos do Instituto Nacional de Pesquisas Espaciais (CPTEC/INPE), que inclui tais medidas de radiâncias feitas pelo sensor que está a bordo dos satélites da série NOAA. A versão operacional do sistema G3DVAR contempla apenas o satélite NOAA-15. Adicionalmente, foram realizados experimentos numéricos que incluíram os satélites NOAA-18 e NOAA-19. É feita uma avaliação sobre a variável temperatura de brilho simulada para os canais sensíveis à superfície terrestre (i), através de uma comparação com observações, e (ii) através da avaliação da equação de transferência radiativa, para os três satélites. Os resultados indicaram que o modelo de transferência radiativa em média superestima a temperatura de brilho nos canais sensíveis à superfície terrestre para os três satélites na região da América do Sul para os meses de verão. Além disso, as observações dos satélites incorporadas no sistema tiveram um aceite superior ao do satélite NOAA-15, de maneira que os satélites NOAA-18 e NOAA-19 podem ser incorporados no modo operacional do Sistema G3DVAR.

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. [ABSTRACT FROM AUTHOR]

In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three-dimensional variational data assimilation system (3DVAR). In particular, we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for 'control' and 'radiance' assimilation cases for the hurricane Sandy period. In 'control' assimilation experiment, only conventional air and surface observations data are assimilated, while, in 'radiance' assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short- to medium-range forecast (up to ~60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation. [ABSTRACT FROM AUTHOR]

In the past, satellite observations of the microwave radiation emitted from the atmosphere have been directly utilized for deriving the climate tends of vertical-layer-averaged atmospheric temperatures. This study presents the 30-year atmospheric temperature trend derived by one-dimensional variational (1D-Var) data assimilation of Microwave Sounding Unit/Advanced Microwave Sounding Unit-A (MSU/AMSU-A) observations. Firstly, the radiance measurements from MSU on board the early National Oceanic and Atmospheric Administration (NOAA)-6 to NOAA-14 and AMSU-A on board NOAA-15 to -19 have been inter-calibrated to form a fundamental climate data record. A 1D-Var method is then employed to establish the thematic climate data record of atmospheric temperature profiles that are appropriate for climate change study. Verification of the MSU/AMSU-A derived temperature profiles with collocated Global Positioning System radio occultation data confirms a reasonable good accuracy of the derived atmospheric temperature profiles in the troposphere and low stratosphere. Finally, the global climate trend of the atmospheric temperature in clear-sky conditions is deduced, showing not only a global warming in the troposphere and a cooling in the stratosphere, but also a stronger warming in the upper troposphere than in the low troposphere. [ABSTRACT FROM AUTHOR]

Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models' three-dimensional variational data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance) as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July 2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture, due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model. [ABSTRACT FROM AUTHOR]

Two sets of assimilation experiments on a landfalling typhoon—Typhoon Dan (1999) over the western North Pacific were designed to compare the performances of two kinds of variational data assimilation schemes that are the 3-Dimensional Variational data assimilation of Mapped observation (3DVM) and the 4-dimensional variational data assimilation (4DVar). Results show that: (1) both the 3DVM and 4DVar successfully improved the simulations of typhoon intensity and track incorporating the satellite AMSU-A retrieved temperature and wind data into the initial conditions, and the 3DVM more significantly due to the flow-dependent of background error covariance matrix and observation error covariance matrix like 3-dimensional variational data assimilation (3DVar) circle; (2) inclusions of extra model integration iterations at each observation time in the 3DVM make it more consistent with prediction model; (3) the 3DVM is much more time-saving due to the exclusion of the adjoint technique in it. [ABSTRACT FROM AUTHOR]

An area, including Luzon and regions near Taiwan, is located on the main path of western North Pacific tropical cyclones (typhoons). The goal of this study was to develop a satellite rainfall algorithm in order to assist in reducing the damage to livelihoods and economies caused by typhoon rainfall. The microwave data from the Advanced Microwave Sounding Unit‐A (AMSU‐A) onboard the National Oceanic & Atmospheric Administration (NOAA)‐15+ were used to estimate oceanic typhoon rainfall over regions near Taiwan. The techniques are to screen rainfall over the oceans by checking cloud liquid water and scattering indices, to classify rain type by using threshold checks and to derive rain intensities from the regressive relationship between satellite microwave brightness temperature ( T b ) and island‐gauge rain rate (RR). The regression of the emission‐based mechanism rainfall used data from AMSU channels at 23.8 and 31.4 GHz regarding the sensitive response to cloud liquid water and to estimate RR over the oceans directly. In addition, the regression of the scattering‐based mechanism rainfall combined low frequency (23.8 and 31.4 GHz) with high frequency (89 GHz) data. As to quantitative validation for the estimated and observed RR, the coefficient of determination is 0.87. Study results showed that one can estimate the rain intensity as typhoon approaching regions near Taiwan, which should increase preparedness against natural calamities in advance. [ABSTRACT FROM AUTHOR]

Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002–2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims to obtain the rate of rainfall (RR) over ocean areas using satellite data by establishing the relationship between Advanced Microwave Sounding Unit-A (AMSU-A) microwave brightness temperature (Tbs) data and island rain gauge rainfall. This study applied a rain identification technique developed by Grody et al. (1999) using AMSU-A data accepted to screen rain events over ocean areas. Using rain gauge data of Sakishima-shoto in Japan as a ground truth, validation of rain identification was conducted every May and June from 1999 to 2001. Including times of both rainfall and no rainfall, the overall rates of successful recognition were 87.3%, 87%, and 89% for 1999, 2000, and 2001, respectively. As for quantitative validation, the coefficient of determination was 0.72. To obtain further precipitation observations, another rainfall estimation retrieved by the TRMM Microwave Imager (TMI) data onboard the Tropical Rainfall Measuring Mission (TRMM) was compared with the AMSU-A data. The results displayed solid agreement in rainfall patterns and rain distribution. Additionally, when the AMSU-A RRs were multiplied by a factor of approximately 1.5 (particularly for heavy rainfalls), the intensities of the AMSU-A RRs roughly equaled those of the TMI RRs. Combining this data with the other satellite rainfall retrieval data, a series of AMSUA rain maps for sea areas were shown to mitigate the problem of relatively sporadic observations of rainfall at sea.

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. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) is a promising approach to improve meteorological and PM 2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM 2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM 2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM 2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM 2.5 episode. This study also helps understand how DA improve the PM 2.5 forecasts over Central China. • Assimilating satellite radiances and conventional observations helps meteorological forecasts get better over Central China. • Based on improved meteorological fields, WRF-Chem model improve PM 2.5 forecasts over the study area. • Data assimilation also helps analyzing the formation of an air pollution episode over Central China. [ABSTRACT FROM AUTHOR]

Due to a shorter effective integration time for each field of view of the Advanced Microwave Temperature Sounder (ATMS) onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite than that for the Advanced Microwave Sounding Unit‐A (AMSU‐A) onboard previous National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites NOAA‐15 to NOAA‐19, ATMS temperature‐sounding channels have higher observational resolutions and larger noise equivalent differential temperatures than the corresponding AMSU‐A channels. The high resolution of the ATMS allows hurricane rainband features that are not resolvable by AMSU‐A to be captured. But the larger noise equivalent differential temperature of ATMS weakens this capability through the significant impact of observational noise on warm‐core retrievals. In this study, a remapping algorithm is applied to obtain AMSU‐A‐like ATMS fields of view to suppress this noise. A modified warm‐core retrieval algorithm, which consists of two sets of training coefficients for clear‐sky and cloudy conditions, is applied to limb‐corrected ATMS and AMSU‐A measurements using collocated Global Positioning System radio occultation observations in the previous month of the targeted hurricanes as training data sets. ATMS channels 5, 6, and 7 (AMSU‐A channels 4, 5, and 6) are excluded when training the coefficients for cloudy conditions to avoid cloud/rain contamination. As a result, the abnormal cold core in the low and middle troposphere and the banded warm structures in phase with rainbands are both successfully removed. The warm‐core evolution of Hurricane Matthew (2016) during its entire life span is temporally consistent on intensity as obtained from NOAA‐15, NOAA‐18, and MetOp‐B AMSU‐A observations and S‐NPP ATMS observations. Key Points: A remapping algorithm is applied to obtain AMSU‐A‐like ATMS fields of viewA rainband‐like artificial warm‐core structure is caused by rain contamination in ATMS channels 5–7Impacts of rain contamination on the warm‐core retrieval results in the low troposphere and rainy areas are removed [ABSTRACT FROM AUTHOR]