Advanced Microwave Sounding Unit-A Research Articles

All‐sky satellite data assimilation of microwave temperature sounding channels at the Met Office

One of the main avenues to improve the skill of a numerical weather prediction system is to increase the accuracy of the initial conditions for prediction through the assimilation of a larger number of observations. This article discusses our work on assimilating cloud‐affected radiances from microwave temperature sounding channels of the Advanced Microwave Sounding Unit A (AMSU‐A) satellite sensor, which are currently underused by the Met Office operational data assimilation system. Despite their importance, as measured by impact indicators such as the Forecast Sensitivity to Observations Impact (FSOI), most cloud‐affected microwave data are discarded so as to avoid detrimental effects, as they are much more difficult to predict than clear‐sky radiances. This is due to shortcomings in parametrization schemes (for example, large‐scale cloud and precipitation, convection) in both nonlinear and linearized prognostic models, as well as radiative transfer models. As first demonstrated at the European Centre for Medium‐Range Weather Forecasts (ECMWF), these difficulties can be eased when observation uncertainties for cloud‐affected radiances are inflated prior to assimilation as a function of cloud amount present in a given observed or predicted scene. Our results show that cloud‐dependent observation uncertainty inflation for cloud‐affected radiances provides beneficial effects on forecast skill over two three‐month‐long “all‐sky” (nonprecipitating) data assimilation trials. In particular, root‐mean‐square error (RMSE) reductions in 500‐hPa geopotential height forecasts of about 1% up to day 2 and in 10‐m wind and 2‐m temperature forecasts up to day 6 have been demonstrated. Also, our experiments show evidence of significant improvements in the fit between observations and short‐range forecasts for lower‐peaking humidity‐ and temperature‐sensitive channels.

An Assessment of Satellite Radiance Data Assimilation in RMAPS

Due to the availability of observations and the effectiveness of bias correction, it is still a challenge to assimilate data from the polar orbit satellites into a limited-area and frequently updated model. This study assessed the initial application of satellite radiance data from multiple platforms in the Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS). Satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) were used. Two 12-day retrospective runs were conducted to evaluate the impact of assimilating satellite radiance data on 0–24 h forecasts using RMAPS. The forecasts, initialized from analyses with and without satellite radiance data, were verified against observations. The results showed that satellite radiance data from AMSU-A and MHS had a positive impact on the initial conditions and the forecasts of RMAPS, even over the relatively data-rich area of North China. Compared to the control run that only assimilated conventional observations, an improvement of about 36.8% can be obtained for the temperature bias between 300 hPa and 850 hPa and 0.65% for the average RMSE. Satellite radiance observations from 1200 UTC contribute relatively significantly (77.8%) to the bias improvement of the initial temperature field. For the wind at 10 m, the bias and root-mean-square error (RMSE) both had a reduction for the 0–12 h forecast range. An improvement can be also found for the skill score of the 3-h accumulated rainfall below 10.0 mm in the first 12 h of the forecast range. There was a slight improvement in the skill score of the 6-h accumulated rainfall above 50 mm over North China, with a 20.7% improvement for the first 12 h of the forecast. The inclusion of satellite radiance observations was found to be beneficial for the initial temperature, which consequently improved the forecast skill of the 0–12 h range in the RMAPS.

Impact of AMSU-A Radiances in a Column Ensemble Kalman Filter

Abstract 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.

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

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

Hurricane Warm‐Core Retrievals from AMSU‐A and Remapped ATMS Measurements with Rain Contamination Eliminated

AbstractDue 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.

Revisiting the Mystery of Recent Stratospheric Temperature Trends

Simulated stratospheric temperatures over the period 1979-2016 in models from the Chemistry-Climate Model Initiative (CCMI) are compared with recently updated and extended satellite observations. The multi-model mean global temperature trends over 1979- 2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K decade-1 for the Stratospheric Sounding Unit (SSU) channels 3 (~40-50 km), 2 (~35-45 km), and 1 (~25-35 km), respectively. These are within the uncertainty bounds of the observed temperature trends from two reprocessed satellite datasets. In the lower stratosphere, the multi-model mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13-22 km) is -0.25 ± 0.12 K decade-1 over 1979-2005, consistent with estimates from three versions of this satellite record. The simulated stratospheric temperature trends in CCMI models over 1979-2005 agree with the previous generation of chemistry-climate models. The models and an extended satellite dataset of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998-2016 compared to the period of intensive ozone depletion (1979-1997). This is due to the reduction in ozone-induced cooling from the slow-down of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite observed stratospheric temperature trends than was reported by Thompson et al. (2012) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of simulated trends is comparable to the previous generation of models.

Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

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

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

Tracing the Error Sources of Global Satellite Mapping of Precipitation for GPM (GPM-GSMaP) Over the Tibetan Plateau, China

This study focuses on tracing the error sources of the latest global satellite mapping of precipitation for global precipitation measurement (GPM-GSMaP) over the Tibetan Plateau by the corresponding satellite information flag provided by the retrieval system. We investigated the characteristics of GPM-GSMaP estimates from 11 passive microwave sensors (including 7 imagers and 4 sounders) and 1 geo-infrared data source integrating the morphing technique (i.e., morph). Assessment results show that imagers are generally superior to sounders and morph. Among the seven types of imagers, the Tropical Rainfall Measuring Mission Microwave Imager, which rarely underestimates heavy rain events, exhibits the best performance with total bias of −8.8%. In contrast, the worst performance is found in the special sensor microwave imager/sounder on defense meteorological satellite program (DMSP)-F19 with the largest bias of approximately 39.8%. The GPM Microwave Imager shows acceptable accuracy in detecting heavy rain, but it tends to overestimate light and moderate rain. Compared to the imagers, all the sounders produced larger biases (>120%), although they show lower probability of missing rainfall events. Particularly, the AMSU-A/MHS (i.e., Advanced Microwave Sounding Unit-A; Microwave Humidity Sounder) on national oceanic and atmospheric administration (NOAA) satellites (i.e., NOAA-18 and −19) display higher biases than those on meteorological operational satellite (MetOp) satellites (i.e., MetOp-A and −B), with dramatic overestimation up to 168.4%. Additionally, the error components of morph show a similar pattern to those of sounders, except for substantial underestimation of heavy rain events. The approach of tracing error reported here can enable both GPM-GSMaP users and developers to better understand the error sources of precipitation retrievals.

Evaluation and Assimilation of the Microwave Sounder MWHS-2 Onboard FY-3C in the ECMWF Numerical Weather Prediction System

This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts’ assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2–4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels.

Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters

In this study, the forecast sensitivity to error covariance parameters was calculated using the forecast sensitivity to observations (FSO) and was employed to adjust the observation error variance in the Korea Meteorological Administration (KMA) Unified Model (UM) four-dimensional variational (4DVAR) system. The error covariance adjustment parameters were estimated by applying a multiple linear regression method to the forecast error reduction (FER) and forecast sensitivity to error covariance parameters in July and August 2012. The adjustment parameters were applied for numerical weather prediction (NWP) in August 2012 using the KMA UM 4DVAR system to validate the adjusted observation error variance in the operational NWP. The results indicated that most observation error variances should be decreased to reduce the forecast error. By decreasing the observation error variance of Advanced Television and Infrared Observational Satellite Operational Vertical Sounder (ATOVS) data by 72.14% for the NWP of August 2012, the residual within the assimilation window (O-A) decreased by 10.62% and that in the 24–29 h forecast range (O-F) decreased by 5.4%; therefore, the analysis and forecast results verified with radiosonde, surface, and satellite observations showed more similar values to all those observations. The upper atmospheric O-F was reduced by approximately 2–3.5% verified by Advanced Microwave Sounding Unit-A, and 21.7% verified by Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) sensors. Therefore, the adjustment of ATOVS observation error variance using the forecast sensitivity to error covariance parameters was effective for reducing the forecast error in the KMA UM 4DVAR system.

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.

Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system

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.

The Stratospheric Changes Inferred from 10 Years of AIRS and AMSU-A Radiances

AbstractGlobal-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 (2σ) 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.

A New Variational Assimilation Method Based on Gradient Information from Satellite Data

With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. A gradient information assimilation method is proposed in this paper to eliminate systematic bias. This method uses a gradient operator for gradient transformation between the model variable and observation variable and reaches the objective of eliminating systematic bias. An ideal experiment of variational data assimilation is conducted using the Community Radiative Transfer Model (CRTM) and Advanced Microwave Sounding Unit-A (AMSU-A) data, indicating that only assimilating gradient information can eliminate the smooth systematic bias in observation data. Then, a numerical simulation of tropical cyclone (TC) Megi and data assimilation experiment are conducted using the Weather Research Forecast (WRF) and WRF Data Assimilation (WRFDA) model as well as the Atmospheric Infrared Sounder (AIRS) data. The results show that the method of gradient information assimilation can improve the accuracy of TC tracks forecast and is also applicable for dealing with unreliable satellite data.

Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts

Since the launch of the NOAA‐15 satellite in 1998, the observations from microwave temperature and humidity sounders have been routinely disseminated to user communities through two separate data streams. In the Advanced Microwave Sounding Unit‐A (AMSU‐A) data stream, brightness temperatures in 15 channels are available primarily for profiling atmospheric temperature from the Earth's surface to the low stratosphere. In the Advanced Microwave Sounding Unit‐B (AMSU‐B) or Microwave Humidity Sounder (MHS) data stream, the brightness temperatures in five channels are included for sounding water vapour in the low troposphere. Assimilation of microwave radiance data in numerical weather prediction systems has also been carried out with AMSU‐A and AMSU‐B (MHS) data in two separate data streams. A new approach is to combine AMSU‐A and MHS radiances into one data stream for their assimilation. The National Centers for Environmental Prediction Gridpoint Statistical Interpolation analysis system and the Advanced Research Weather Research and Forecast model are employed for testing the impacts of the combined datasets. It is shown that the spatial collocation between MHS and AMSU‐A fields of view in the one data stream experiment allows for an improved quality control of MHS data, especially over the conditions where the liquid‐phase clouds are dominant. As a result, a closer fit of analyses to AMSU‐A and MHS observations is obtained, especially for AMSU‐A surface‐sensitive channels. The quantitative precipitation forecast skill is improved over a 10‐day period when Hurricane Isaac made landfall.

All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System

AbstractThe capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study.In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the levels of cloud variability from the ensemble forecast in single- and dual-resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west coasts of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1–5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of 12 May 2016.

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

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

Evolution of the upper‐level thermal structure in tropical cyclones

AbstractTropical cyclones (TCs) are associated with tropopause‐level cooling above tropospheric warming. We collect temperature retrievals from 2007 to 2014 near worldwide hurricane‐strength TCs using three remote sensing platforms: the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), the Advanced Microwave Sounding Unit‐A (AMSU‐A), and geostationary infrared (IR) imagery. These retrievals are composited about the lifetime maximum intensity (LMI) to examine the evolution of the fine‐scale temperature structure within TCs. The convective structure evolves highly asymmetrically about LMI, while intensity evolution shows a much weaker degree of asymmetry. Relative to the far‐field structure, tropopause‐level cooling occurs before a tropospheric warm core is established. We speculate that the associated convective destabilization exerts a positive feedback on TC development by increasing the depth of existing convection. Tropopause‐level cold anomalies move away from the storm after LMI, potentially increasing the near‐surface horizontal pressure gradient toward the storm center and increasing the maximum winds.

Microwave Sounder Cloud Detection Using a Collocated High-Resolution Imager and Its Impact on Radiance Assimilation in Tropical Cyclone Forecasts

AbstractAccurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.