Advanced Technology Microwave Sounder Data Research Articles

Comparison of tropical cyclone thermal structures derived from ATMS and synthetic AMSU-A/MHS

The warm core of a tropical cyclone (TC) reflects its intensity and evolution. In the past, microwave sounding data have been widely used for detecting the TC warm core. However, the observed magnitude of the TC warm core can vary with instruments. This study utilizes the oversampling data from the Advanced Technology Microwave Sounder (ATMS) to reconstruct the brightness temperature data consistent with the data from Advanced Microwave Sounding Unit-A (AMSU-A), which is referred to as AMSU-like data. Through comparisons, it is found that brightness temperature from the original ATMS has more detailed structures, depicting well the TC eye region and cloud bands. Both ATMS and AMSU-like data are used as inputs to the Global Scene-Dependent Atmospheric Retrieval Testbed (GSDART) to retrieve the warm cores of multiple TCs. At 250 hPa, the warm-core intensity retrieved using ATMS data is about 1–2 K higher than that from AMSU-like data and the warm-core structure from ATMS is more detailed. It is recommended that the warm-core intensity retrieved from AMSU-A be corrected to the ATMS values for multi-sensor monitoring of TCs using our newly defined relationship.摘要热带气旋(TC)的暖心反映了其强度和变化. 微波探测数据被广泛用于探测TC暖心, 但观测到的 TC 暖心强度可能因仪器而异. 本研究利用先进技术微波探测仪 (ATMS) 的过采样数据重采样至与先进微波探测仪 (AMSU-A) 一致的亮温, 称为类-AMSU数据. 通过对比发现, ATMS的观测更加细致, 较好地刻画了TC眼区和云带. 使用ATMS和类-AMSU数据反演多个TC的暖心发现, 在250 hPa, 使用ATMS数据反演的暖心强度比类-AMSU高约1–2K, 并且其暖心结构更详细.

Retrieval of temperature profiles in tropical cyclone Nanmadol from resampled advanced technology microwave sounder observations

In the past, satellite microwave sounding data are often utilized for retrieving the thermal structure of tropical cyclones (TCs). However, the spatial resolutions and scan pattern of the instruments vary from one to another and can affect the retrievals of TC structures. In this study, Backus-Gilbert Inversion (BGI), ATOVS and AVHRR Pre-processing Package (AAPP) filter and N × N field-of-views (FOVs) average algorithms are utilized to resample the Advanced Technology Microwave Sounder (ATMS) data and the impacts from different resampling algorithms on brightness temperature structures and retrieved atmospheric temperature are analyzed. It is shown that for ATMS channel 1 to 2, the larger differences between original and resampled brightness temperatures are found in cloudy areas and at the land-ocean boundary. When ATMS channels 1–16 data are resampled to the AMSU-like resolution, the brightness temperatures become less noisy at channel 3 to 16 and can lead to a higher convergence rate in the one-dimension variational (1DVAR) algorithm. Furthermore, the warm core intensity of a tropical storm of Nanmadol is reduced by about 2.5 K and its size becomes smaller after the ATMS data is resampled to the AMSU resolution.

Characteristics of Strong Storms at the Pre‐Convection Stage From Satellite Microwave Sounder Observations

AbstractHigh‐temporal‐resolution geostationary satellite infrared measurements are always used to capture and predict typical characteristics at the cloud top of rapidly developing strong storms at the pre‐convection or convection initiation (CI) stage. However, the large false alarm rate of CI nowcasting is difficult to avoid due to the complex and unpredictable trigger factors. Although the microwave measurement technique can observe thick clouds and even the precipitation within clouds due to the weaker atmospheric extinction effect on microwave, microwave data from polar‐orbiting satellites are rarely used to observe the CI due to their relatively low temporal resolution. In this study, we analyze several previously unknown CI characteristics over the East Asia region from 2016 to 2019 based on spatially and temporally matched Advanced Technology Microwave Sounder data. These typical CI samples are initially identified by using continuous infrared images from the Himawari‐8 geostationary satellite. The results show that there is a distinct cloud optical depth at the pre‐convection stage in the western (deep cloud clusters) and eastern (shallow cloud clusters) Tibetan Plateau (TP). The shallow precipitating cloud clusters of the CI over the eastern TP are possibly attributed to the favorable local dynamic and thermal conditions stem from the Asian monsoon. Another notable finding shows that the fast‐developing CI over the ocean has thick clouds compared with the samples over the land. Overall, the unique CI characteristics found from microwave observations in this study indicate that the future geostationary microwave sounder technologies will almost certainly provide some new findings and enhance early warning capabilities about convection.

Intercomparison of Resampling Algorithms for Advanced Technology Microwave Sounder (ATMS)

The observations from satellite microwave-sounding instruments have been widely used in weather and climate studies. Since the data resolution varies with frequency and satellite viewing angle, it is normally required that the measurements at each frequency be resampled to obtain a uniform resolution prior to various applications. In this study, the ATOVS and AVHRR pre-processing package (AAPP) Fourier transform algorithm is modified for ATMS data and the results are compared with those derived from Backus–Gilbert inversion (BGI) and the original AAPP. From the simulated and observed ATMS data, we demonstrated the new algorithm has better results in terms of imaging quality and noise suppression, compared with BGI and AAPP. In general, our modified AAPP algorithm reduces the error by at least about 0.5 K in ATMS channels 2 and 6 and at all the viewing angles.

In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets

AbstractThe National Oceanic and Atmospheric Administration (NOAA) Microwave Integrated Retrieval System (MiRS) algorithm has been producing retrieval products from NOAA‐20 Advanced Technology Microwave Sounder (ATMS) data since shortly after launch in late 2017. Retrieval products from the Suomi National Polar‐orbiting Partnership (SNPP) ATMS data have been similarly available since 2012. Among the products routinely generated is Total Precipitable Water (TPW), based on the vertical integral of the MiRS retrieved water vapor profile. In this study, TPW data from NOAA‐20 ATMS have been the subject of an in‐depth evaluation. The evaluation included characterization of dependence on orbital node (ascending/descending), season, surface type, scan angle, and meridional (zonally averaged) variation. Due to the similarity between NOAA‐20 and SNPP derived TPW, the emphasis here is on the NOAA‐20 evaluation. The globally averaged values of TPW for 2019 are 25.41 and 25.39 mm from NOAA‐20 and SNPP ATMS, respectively, for combined orbits (ascending and descending). For combined orbits, the bias and standard deviation of MiRS NOAA‐20 ATMS TPW with respect to European Centre for Medium‐Range Weather Forecasts/Global Data Assimilation System (ECMWF/GDAS) analyses are 0.79/1.76 mm and 3.65/3.55 mm, respectively, at 10°N where part of annual mean Intertropical Convergence Zone (ITCZ) is located, while at 40°N, corresponding bias and standard deviation values are 0.84/0.67 mm and 3.00/2.55 mm, respectively. The yearly averaged horizontal distribution of MiRS TPW retrieved from NOAA‐20 ATMS is highly correlated with that of ECMWF and GDAS analyses data, with a correlation coefficient of ∼1.

Correcting Calibration Drifts Using Solar and Lunar Intrusions for Miniaturized Microwave Radiometers

CubeSats with miniaturized microwave radiometers are now demonstrating the potential to provide science-quality weather measurements. For example, the Micro-Sized Microwave Atmospheric Satellite-2A (MicroMAS-2A) and Temporal Experiment for Storms and Tropical Systems–Demonstration (TEMPEST-D) CubeSats are both launched in 2018 and have demonstrated microwave atmospheric sounder data from orbit. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a follow-on constellation of six 3U CubeSats based on the MicroMAS-2 design that is scheduled for launch no sooner than 2021. The TROPICS sensors use internal noise diodes (NDs) for calibration. Although the NDs on TROPICS are similar to technology flown on GMI, they have not been tested on-orbit at TROPICS frequencies. In order to track and correct ND drift, we develop a novel method of calibration for CubeSat constellations, such as TROPICS, by incorporating periodic solar and lunar intrusions as an additional source of information to counter ND drift. These lunar intrusions also occur for existing satellites hosting microwave radiometers in sun-synchronous polar orbits but are much more infrequent than for the TROPICS constellation’s scanning payload. In this work, we develop a solar/lunar calibration correction algorithm and test it using advanced technology microwave sounder (ATMS) lunar intrusion data. The mean bias and standard deviation between the solar/lunar calibration correction algorithm and actual ATMS data fall within the expected ATMS error budget of 0.6–3.9 K, validating our model.

JPSS-1 ATMS Postlaunch Active Geolocation Analysis

A NOAA-20 (N20) Advanced Technology Microwave Sounder (ATMS) active geolocation (GEO) test was performed around January 2018 with 24 preselected coastline crossing scenes. After a comprehensive analysis of ATMS stare data and the corresponding Visible Infrared Imaging Radiometer Suite (VIIRS) data, the ATMS pitch, roll, and yaw pointing angle errors are found from the nadir perpendicular, the nadir oblique shallow angle, and the off-nadir perpendicular coastline crossing data, respectively. In this study, we first determine the ATMS radiometric coastline crossing time by using the ATMS radiometric count data. Since the coastline can be located anywhere within one ATMS field of view (FOV) from the passive (regular scanning) GEO data, depending on the scan starting time, it is not a valid assumption for the inflection point being the same as the coastline location. Consequently, using the passive GEO data to validate the sensor’s on-orbit pointing angle performance is limited. After finding the ATMS radiometric coastline crossing time from the ATMS data, we compare it with the VIIRS effective time stamp. Specifically, the VIIRS M3, M4, and M5 (true color) and M15 and M16 bands (thermal) data have a much smaller footprint size. Using the differences of the ATMS and VIIRS (effective) coastlines crossing times, the N20 ATMS pitch, roll, and yaw pointing angle errors are found to be −0.09°, −0.24°, and 0.28°, respectively. To determine ATMS GEO properly, these on-orbit pointing errors need to be corrected, adding to the ATMS sensor data record (SDR) processing coefficient table, and passed on to the operational GEO processing code.

Gap Filling of Advanced Technology Microwave Sounder Data as Applied to Hurricane Warm Core Animations

AbstractAs a microwave radiometer seeing through clouds, Advanced Technology Microwave Sounder (ATMS) observations play a critical role in visually monitoring hurricane warm core structures. However, the presence of orbit gaps within the ATMS observations at low latitudes regions, where hurricanes frequently develop, raises concerns in monitoring hurricanes' spatial variability. To resolve this issue, this study generates gap‐filled ATMS brightness temperature data by using the smoothing algorithm of Penalized Least Square Discrete Cosine Transform. The accuracies of the missing‐filled brightness temperatures for temperature sounding channels are approximately within 1 K. Furthermore, the gap‐filled brightness temperature data from channels 5–12 are utilized to establish a three‐dimensional Hurricane Warm Core Animation System (HWCAS) in near real time (NRT), which helps to visually observe realistic warm core structures of a hurricane system. The information of hurricane warm core over open oceans and coastal areas is derived using a combination of three new regression‐based atmospheric temperature retrieval algorithms, with the averaged error typically within ±1 K at the vertical levels a warm core could occur. Each animation consists of 97 two‐dimensional atmospheric temperature anomaly images at different cross‐sections through hurricane core regions. The retrieved maximum temperature anomalies show well the formation, intensification, weakening, reintensification, and dissipation stages of Hurricane Florence that are similar to those from the European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis. They also show similar weakening and reintensifying stages to those of the maximum sustained winds by the best track data, albeit with some temporal lead/lag. With its strength in NRT hurricane monitoring, the HWCAS demonstrate its great potential in providing meteorologists with timely information of temperature anomaly fields in the inner‐core regions of a hurricane.

A physical algorithm for precipitable water vapour retrieval over land using passive microwave observations

ABSTRACT Precipitable water vapour (PWV) plays an important role in weather prediction, hydrological cycles and climate change. In this study, a physics-based algorithm was proposed to retrieve PWV over land using satellite passive microwave observations near 165.50 GHz and 183.31 GHz, which have higher spatial resolution and lower sensitivity to land surface emissivity than low-frequency channels. The algorithm uses a perturbation formulation of simplified radiative transfer equation to derive PWV and at-surface brightness temperature (BT) in 165.50 GHz simultaneously. It directly derives the at-surface BT instead of land surface temperature, so the algorithm needs no prior information of surface emissivity. Sensitivity analysis and retrieval experiments from simulated data were carried out for Advanced Technology Microwave Sounder (ATMS) onboard the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite. The results showed that the algorithm using ATMS channels 17, 18 and 19 measurements can significantly reduce the PWV retrieval errors caused by surface emissivity uncertainty. The coefficient of determination (R 2), root-mean-square error (RMSE), and bias of the retrieved PWV from ATMS data by the proposed algorithm and SuomiNet Global Positioning System (GPS) PWV were 0.90, 0.43 cm and −0.02 cm, respectively. In addition, the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud products were used to evaluate the effects of cloud on the PWV retrievals. The results showed that the presence of cloud does not decrease the accuracy of the PWV retrievals.

Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS)

One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.

Comparison of ATMS Striping Noise Between NOAA-20 and S-NPP and Noise Impact on Warm Core Retrieval of Typhoon Jelawat (2018)

The Advanced Technology Microwave Sounder (ATMS) is onboard both the National Oceanic and Atmospheric Administration (NOAA)-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellites. NOAA-20 has the same sun-synchronous orbit as that of the S-NPP, but is 50 min (i.e., half orbit) ahead. The striping noise is found in ATMS brightness temperature observations from both NOAA-20 and S-NPP. In this study, first, a striping noise detection and mitigation algorithm that was previously developed for striping noise mitigation in ATMS observations from S-NPP is adopted to characterize the striping noise in NOAA-20 ATMS brightness temperature measurements. It combines a principal component analysis and an ensemble empirical mode decomposition method. It is found that the magnitudes of both the striping noise and the random noise in NOAA-20 ATMS data are smaller than those in S-NPP ATMS data. Second, global positioning system radio occultation retrieved temperature profiles are used as the training dataset for ATMS hurricane warm core retrievals in order to investigate the impacts of the data noise. Numerical results are demonstrated using the case of Typhoon Jelawat (2018), which rapidly intensified from a Category 1 to a Category 4 super typhoon and weakened back to Category 1 within 24 h. Finally, we show that a half-orbit separation of NOAA-20 from S-NPP enables the rapidly evolving vertical structures of Typhoon Jelawat. This suggests an enhanced tropical cyclone monitoring capability offered by NOAA-20 and S-NPP for this hurricane season and a few following years.

Bias characterization of ATMS low-level channels under clear-sky and cloudy conditions

The Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership satellite is a cross-track scanning instrument containing 22 sounding channels in total. In this study, the bias characteristics of channels 1–6, which could have significant cloud contamination in heavy precipitation, are first analyzed based on the differences between ATMS observations (O) and model simulations (B) under clear-sky conditions over oceans. Latitudinal dependencies of the biases of window channels 1–3 are greater than those of channels 4–6. Biases of all nadir-only observations examined in different latitudinal bands [μ1(φ)] are positive and no more than 7.0 K. Biases at higher latitudes are larger. Channels 1–5 have a generally symmetric scan bias pattern [μ2(α)]. The global distributions of brightness temperature differences after subtracting the biases, i.e., O-B-μ1(φ)-μ2(α), for channels 3–6 spatially match the liquid water path distributions. Excluding ice-affected observations, channel 3–6 O-B differences systematically increase as the liquid water path increases under cloudy conditions. Further investigation is needed to apply these findings for ATMS data assimilation under both clear-sky and cloudy conditions.

A Modified Ice Water Path Retrieval Algorithm Applicable to the ATMS

The algorithm to retrieve the ice water path (IWP) from Microwave Humidity Sounder (MHS) measurements of two window channels at 89 and 157 GHz was already developed, and the IWP retrieval products have been made operationally available since NOAA-15. An adaptation of the same algorithm to the Advanced Technology Microwave Sounder (ATMS) is not straightforward due to differences of central frequencies and fields-of-view (FOVs) of the window channels between ATMS and MHS. In this study, ATMS and MHS orbit data from May 2016 to April 2017 are first collocated using the Simultaneous Nadir Overpass (SNO) method. Then two linear relationships between ATMS and MHS window channels are established using the SNO data: one between MHS channel 1 (89.0 GHz, 15-km nadir resolution) and ATMS channel 16 (88.2 GHz, 32-km nadir resolution), and the other between MHS channel 2 (157.0 GHz, 15-km nadir resolution) and ATMS channel 17 (165.5 GHz, 16-km nadir resolution). Since MHS IWPs are calculated differently when the scattering parameters are small (<0.15 or <0.18) or large (0.15 and 0.18), the histogram of IWP is found to have an unrealistic local minimum around 0.4 kg m−2. A modification is made to use the same algorithm as MHS when the scattering parameter at 89 GHz <0.08 or >0.19, and a linear interpolation within the interval of . The ATMS IWP is then derived using the modified algorithm by obtaining equivalent MHS brightness temperatures according to the regression relationships, while the MHS IWP is derived directly using the modified algorithm. Also, the ATMS results are collocated with MHS orbits for comparison purposes. The spatial distributions of ATMS- and MHS-derived IWPs are consistent. A quantitative analysis shows that the two results are approximately same with deviations smaller than about 0.02 kg m−2.

All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study

Abstract Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

Mapping coastal floods induced by hurricane storm surge using ATMS data

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

On the Use of Gaussian Random Processes for Probabilistic Interpolation of CubeSat Data in the Presence of Geolocation Error

With their greatly reduced sizes, low development cost, and rapid construction time, CubeSats have merged as a platform of considerable interest for a wide range of applications, including remote sensing. Many applications require the interpolation of sensor data into a regularly spaced grid for the development of downstream scientific products. This problem is complicated for CubeSat platforms due to potentially significant uncertainties associated with the spatial position of the satellite. In this paper, we present a probabilistic approach to the data interpolation problem in which we estimate both the platform location and data samples on a regular grid given observations corrupted by noise and location error. Our approach is based on a Gaussian process model to connect the measured data to the values on the grid. Two statistical models for positional uncertainties are considered, one based on an assumption of independent errors and another motivated by positional errors associated with a specific platform of interest, the MicroMAS radiometer. In each case, the maximum a posteriori estimate of the positions and the data is generated using an optimized Gaussian process regression (OGPR) method resulting in two algorithms: OGPR-IID and OGPR-PCA. The performance of this approach is tested on both simulated data and advanced technology microwave sounder data where significant improvements both qualitatively and quantitatively relative to traditional interpolation methods are observed.

Characterization of geolocation accuracy of Suomi NPP Advanced Technology Microwave Sounder measurements

AbstractThe Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite has 22 channels at frequencies ranging from 23 to 183 GHz for probing the atmospheric temperature and moisture under all weather conditions. As part of the ATMS calibration and validation activities, the geolocation accuracy of ATMS data must be well characterized and documented. In this study, the coastline crossing method (CCM) and the land‐sea fraction method (LFM) are utilized to characterize and quantify the ATMS geolocation accuracy. The CCM is based on the inflection points of the ATMS window channel measurements across the coastlines, whereas the LFM collocates the ATMS window channel data with high‐resolution land‐sea mask data sets. Since the ATMS measurements provide five pairs of latitude and longitude data for K, Ka, V, W, and G bands, respectively, the window channels 1, 2, 3, 16, and 17 from each of these five bands are chosen for assessing the overall geolocation accuracy. ATMS geolocation errors estimated from both methods are generally consistent from 40 cases in June 2014. The ATMS along‐track (cross‐track) errors at nadir are within ±4.2 km (±1.2 km) for K/Ka, ±2.6 km (±2.7 km) for V bands, and ±1.2 km (±0.6 km) at W and G bands, respectively. At the W band, the geolocation errors derived from both algorithms are probably less reliable due to a reduced contrast of brightness temperatures in coastal areas. These estimated ATMS along‐track and cross‐track geolocation errors are well within the uncertainty requirements for all bands.

An Assessment of Data from the Advanced Technology Microwave Sounder at the Met Office

An appraisal of the Advanced Technology Microwave Sounder (ATMS) for use in numerical weather prediction (NWP) is presented, including an assessment of the data quality, the impact on Met Office global forecasts in preoperational trials, and a summary of performance over a period of 17 months operational use. After remapping, the noise performance (NEΔT) of the tropospheric temperature sounding channels is evaluated to be approximately 0.1 K, comparing favourably with AMSU-A. However, the noise is not random, differences between observations and simulations based on short-range forecast fields show a spurious striping effect, due to 1/f noise in the receiver. The amplitude of this signal is several tenths of a Kelvin, potentially a concern for NWP applications. In preoperational tests, adding ATMS data to a full Met Office system already exploiting data from four microwave sounders improves southern hemisphere mean sea level pressure forecasts in the 2- to 5-day range by 1-2%. In operational use, where data from five other microwave sounders is assimilated, forecast impact is typically between −0.05 and −0.1 J/kg (3.4% of total mean impact per day over the period 1 April to 31 July 2013). This suggests benefits beyond redundancy, associated with reducing already small analysis errors.

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.

Evaluation and assimilation of ATMS data in the ECMWF system

This paper reports on the first experiences with Advanced Technology Microwave Sounder (ATMS) data at the European Centre for Medium‐Range Weather Forecasts (ECMWF), both in terms of the contribution to the calibration/validation exercise and in terms of initial assimilation trials. Comparisons in antenna temperature space against short‐term forecasts are used to establish the fidelity of the data. Monitoring of ATMS data against short‐term forecasts shows that the data are generally of good quality, with a noise performance that is well within specification and after appropriate averaging, comparable to or better than that of the Advanced Microwave Sounding Unit‐A (AMSU‐A). Biases vary smoothly with scan positions, even before an appropriate antenna pattern correction has been established, and ATMS looks better than AMSU‐A in this regard. Outer scan positions can be assimilated without restrictions due to biases, and together with the wider swath, this leads to a better coverage from one ATMS compared to one AMSU‐A. There are indications of larger interchannel and spatial error correlations in ATMS data than for AMSU‐A, possibly linked to a weak striping effect. The analysis and forecast impact in initial assimilation trials over two seasons are significantly positive in the short range over the Southern Hemisphere and in the long range over the Northern Hemisphere, with an otherwise overall neutral impact. Experiments in the context of a depleted observing system suggest that ATMS gives a comparable forecast impact to that from a single AMSU‐A/Microwave Humidity Sounder combination.