Advanced Microwave Sounding Unit-A Data Research Articles

Primary Impact Evaluation of Surface Temperature Observations for Microwave Temperature Sounding Data Assimilation over Land

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land.

Estimation of AMSU-A and MHS Antenna Emission from MetOp-A End-of-Life Deep Space View Test

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.

The Effect of Assimilating AMSU-A Radiance Data from Satellites and Large-Scale Flows from GFS on Improving Tropical Cyclone Track Forecast

This study aimed to investigate the effect of assimilating either AMSU-A radiance data from satellites, large-scale flows from the Global Forecast System (GFS), or both together, on improving the track forecast of tropical cyclone (TC). The scale-selective data assimilation (SSDA) approach was employed for the assimilation of large-scale GFS flows, while the conventional 3D variational data assimilation (3DVAR) method was used for that of AMSU-A radiance data. The results show that assimilating either AMSU-A radiance data or large-scale GFS flows has a significant improvement on TC track forecast, but the improvement occurs within the first 72 h and after 72 h, respectively. When assimilating both AMSU-A radiance data and large-scale GFS flows, the forecast can take advantage of both data and thus lead to the smallest 5-day mean errors of the track forecast. These results are instructive to future operational TC track forecasting.

Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

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.

Combining FY-3D MWTS-2 with AMSU-A Data for Inter-Decadal Diurnal Correction and Climate Trends of Atmospheric Temperature

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.

Using a network of temperature lidars to identify temperature biases in the upper stratosphere in ECMWF reanalyses

Abstract. To advance our understanding of the stratosphere, high-quality observational datasets of the stratosphere are needed. It is commonplace that reanalysis datasets are used to conduct stratospheric studies. However, the accuracy of these reanalyses at these heights is hard to infer due to a lack of in situ measurements. Satellite measurements provide one source of temperature information. As some satellite information is already assimilated into reanalyses, the direct comparison of satellite temperatures to the reanalysis is not truly independent. Stratospheric lidars use Rayleigh scattering to measure density in the middle and upper atmosphere, allowing temperature profiles to be derived for altitudes from 30 km (where Mie scattering due to stratospheric aerosols becomes negligible) to 80–90 km (where the signal-to-noise ratio begins to drop rapidly). The Network for the Detection of Atmospheric Composition Change (NDACC) contains several lidars at different latitudes that have measured atmospheric temperatures since the 1970s, resulting in a long-running upper-stratospheric temperature dataset. These temperature datasets are useful for validating reanalysis datasets in the stratosphere, as they are not assimilated into reanalyses. Here, stratospheric temperature data from lidars in the Northern Hemisphere between 1990–2017 were compared with the European Centre for Medium-Range Weather Forecasts ERA-Interim and ERA5 reanalyses. To give confidence to any bias found, temperature data from NASA's EOS Microwave Limb Sounder were also compared to ERA-Interim and ERA5 at points over the lidar sites. In ERA-Interim a cold bias of −3 to −4 K between 10 and 1 hPa was found when compared to both measurement systems. Comparisons with ERA5 found a small bias of magnitude 1 K which varies between cold and warm bias with height between 10 and 1 hPa, indicating a good thermal representation of the middle atmosphere up to 1 hPa. A further comparison was undertaken looking at the temperature bias by year to see the effects of the assimilation of the Advanced Microwave Sounding Unit-A (AMSU-A) satellite data and the Constellation Observing System for Meteorology, Ionosphere, and Climate GPS Radio Occultation (COSMIC GPSRO) data on stratospheric temperatures within the aforementioned ERA analyses. It was found that ERA5 was sensitive to the introduction of COSMIC GPSRO in 2007 with the reduction of the cold bias above 1 hPa. In addition to this, the introduction of AMSU-A data caused variations in the temperature bias between 1–10 hPa between 1997–2008.

Calibration and Validation of Antenna and Brightness Temperatures from Metop-C Advanced Microwave Sounding Unit-A (AMSU-A)

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.

Impacts of Multiple Radiance Data Assimilation on the Simulation of Typhoon Chan-Hom

With the module of assimilating AMSU-A (Advanced Microwave Sounding Unit-A) and AIRS (Atmospheric Infrared Sounder) data in the WRFDA (Weather Research and Forecasting Model Data Assimilation) system, the impacts of joint assimilation of the radiance observations from two satellites on the simulation of typhoon Chan-hom (2015) are addressed. For comparison, experiments with the assimilation of solely GTS (Global Telecommunications System) data, AMSU-A data, or AIRS data are also performed. The results show that, compared to other experiments, the analysis field after assimilating multiple radiance data is closer to the observation. The simulated steering flow in its forecast field is conductive to the northeast twist of the typhoon. In addition, the simulated rainband and the FSS (fraction skill score) calculated from the experiment with assimilating multiple radiance data are better. In the deterministic forecast, better performance is obtained from the simulation with multiple radiance data in the forecast of track, MSLP (minimum sea level pressure), and MSW (maximum surface wind).

Diurnal Variation in Cloud Liquid Water Path Derived from Five Cross-Track Microwave Radiometers Onboard Polar-Orbiting Satellites

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.

Impacts of AMSU-A inter-sensor calibration and diurnal correction on satellite-derived linear and nonlinear decadal climate trends of atmospheric temperature

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 Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter

A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous three-hourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements (RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours.

Development of a New Algorithm to Identify Clear Sky MSU Data Using AMSU-A Data for Verification

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.

Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau

Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau

Assessment and Adaptive Correction of Observations in Atmospheric Sounding Channels of the Satellite Microwave Radiometer MTVZA-GY

The microwave radiometer MTVZA-GY on board the Russian polar orbiting meteorological satellite Meteor-M N2 is briefly described. Errors and biases in antenna brightness temperatures are documented. Observation errors are found to be dependent on the solar angles. An adaptive bias correction technique for MTVZA-GY antenna temperatures is motivated and developed. The technique accounts for the solar angles and sequentially assimilates observed minus simulated radiances in a perpetual 24 h cycle in order to estimate up-to-date correction coefficients defined to be functions of the zenith and azimuth solar angles. The simulated radiances are computed by the RTTOV radiative transfer model from three-dimensional numerical weather prediction fields. The correction technique is implemented for atmospheric temperature and humidity sounding channels of MTVZA-GY. The corrected observations are shown to be significantly more accurate as compared with raw antenna temperatures and with observations that undergo simpler and more traditional corrections. The accuracy of corrected MTVZA-GY observations is compared with the accuracy of AMSU-A and MHS data.

Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data

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.

Dependence of positive refractivity bias of GPS RO cloudy profiles on cloud fraction along GPS RO limb tracks

We collected global COSMIC RO data from 2009 and 2010 using measurements from the NOAA-18 Advanced Microwave Sounding Unit-A (AMSU-A) along GPS RO limb tracks and also using data from the CloudSat Cloud Profiling Radar (CPR). The collocated AMSU-A liquid water path (LWP) retrieval over the ocean is then used to quantify the dependence of fractional refractivity bias (N-bias) of GPS RO profiles in cloudy conditions on AMSU-A data points with nonzero LWP along GPS RO limb tracks.The collocated CPR cloud type dataset is used for selecting GPS RO profiles in cloudy conditions and for grouping GPS RO cloudy profiles into seven different cloud types. It is shown that the positive fractional N-bias varies with cloud fraction along the COSMIC GPS RO limb tracks. It reaches a value between 1 and 2 % when cloud fraction is as high as 90–100 % for altocumulus, altostratus, cirrus, cumulus, and deep convection clouds. For nimbostratus and stratocumulus clouds, large biases are found at any value of cloud fraction. The positive fractional N-bias can be more than 2 % even if cloud fraction is less than about 50 % for nimbostratus and stratus clouds.

Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI–WRF over the western United States

Abstract. Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regional model, six experiments are designed by (1) a control experiment (CTRL) and five data assimilation (DA) experiments with different data sets, including (2) conventional data only (CON); (3) microwave data (AMSU-A + MHS) only (MW); (4) infrared data (IASI) only (IR); (5) a combination of microwave and infrared data (MWIR); and (6) a combination of conventional, microwave and infrared observation data (ALL). One-month experiments in July 2012 and the impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI–WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest root mean square error (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA produced the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA gave a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and in the UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMSEs are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMSE in the moisture forecast, although the smallest bias is found in the LT and MT.

Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

Validation of AMSU-A measurements from two different calibrations in the lower stratosphere using COSMIC radio occultation data

GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission were used to validate the measurements of the advanced microwave sounding unit-A (AMSU-A) in the lower stratosphere from different satellites. AMSU-A observations from two different calibrations—the pre-launch operational and post-launch simultaneous nadir overpass (SNO) calibrations—were compared to microwave brightness temperatures (Tb) simulated from COSMIC data. Observations from three satellites (NOAA-15, -16, and -18) were used in the comparison. The results showed that AMSU-A Tb measurements from both calibrations and from all three NOAA satellites were underestimated in the lower stratosphere, and that the biases were larger in polar winters, especially over the southern high latitudes. In comparison to operational calibration, the SNO-calibrated AMSU-A data produced much smaller biases relative to the COSMIC data. The improvement due to SNO calibration was quantified by a Ratio index, which measured the bias changes from operational to SNO calibrations relative to the biases between the operational-calibrated AMSU-A data and the COSMIC data. The Ratio values were 70 % for NOAA-15 and >80 % for NOAA-18 and -16, indicating that the SNO calibration method significantly reduced AMSU-A biases and effectively improved AMSU-A data quality.