Special Sensor Microwave Imager Research Articles

Global total precipitable water variations and trends over the period 1958–2021

Abstract. Global responses of the hydrological cycle to climate change have been widely studied, but uncertainties still remain regarding water vapor responses to lower-tropospheric temperature. Here, we investigate the trends in global total precipitable water (TPW) and surface temperature from 1958 to 2021 using ERA5 and JRA-55 reanalysis datasets. We further validate these trends using radiosonde from 1979 to 2019 and Atmospheric Infrared Sounder (AIRS) and Special Sensor Microwave Imager/Sounder (SSMIS) observations from 2003 to 2021. Our results indicate a global increase in total precipitable water (TPW) of ∼ 2 % per decade from 1993–2021. These variations in TPW reflect the interactions of global warming feedback mechanisms across different spatial scales. Our results also revealed a significant near-surface temperature (T2 m) warming trend of ∼ 0.15 K decade−1 over the period 1958–2021. The consistent warming at a rate of ∼ 0.21 K decade−1 after 1993 corresponds to a strong water vapor response to temperature at a rate of 9.5 % K−1 globally, with land areas warming approximately twice as fast as the oceans. The relationship between TPW and T2 m showed a variation of around 6 % K−1–8 % K−1 in the 15–55° N latitude band, aligning with theoretical estimates from the Clausius–Clapeyron equation.

Global evaluation of fast radiative transfer model coefficients for early meteorological satellite sensors

Abstract. RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independent dataset of 25 000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historical satellite instruments: the InfraRed Interferometer Spectrometer D (IRIS-D), Satellite Infrared Spectrometer B (SIRS-B), Medium Resolution Infrared Radiometer (MRIR) and High Resolution Infrared Radiometer (HRIR) for the infrared part of the spectrum, and the Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave – Humidity (SSM/T-2), Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager/Sounder (SSMI/S) for the microwave. Simulated channel brightness temperatures show similar statistics for both the independent and the 83-profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between the RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0.4 ± 0.2 K for the infrared and 0.04 ± 0.13 K for the microwave. Examination of the latitudinal dependence of the bias reveals different patterns of variability for similar channels on different instruments, such as the channel centred at 679 cm−1 on both IRIS-D and SIRS-B, showing the importance of the specification of the instrumental spectral response functions (ISRFs). Maximum differences of up to several kelvin are associated with extremely non-typical profiles, such as those in polar or very hot regions.

Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data

Abstract Passive-microwave sea ice concentration (SIC) algorithms employ different frequencies and polarisations in their operational implementations. Commonly, these algorithms utilise combinations such as 19/37 GHz, yielding reduced measurement uncertainties but at a coarse spatial resolution. Alternatively, these algorithms can solely use 89 GHz, producing a higher spatial resolution but with increased measurement uncertainties. This study evaluates the application of a resolution-enhancing SIC algorithm (reSICCI3LF), initially developed for the coarser Special Sensor Microwave Imager / Sounder, on the Advanced Microwave Scanning Radiometer. By applying reSICCI3LF, we aim to produce a 5 km SIC for 2013–2020 in the Fram Strait and the Barents and Kara Sea region that gains the benefits of both types of algorithms, high spatial resolution and low measurement uncertainty. We present the algorithm tuning, spectral analysis of spatial resolutions, and validation against the Round Robin Data Package of 0% and 100% SIC points and SIC derived from Landsat-8. The findings demonstrate that the reSICCI3LF algorithm produces a SIC field with fine details, achieving a balance between high spatial resolution and lower measurement uncertainties compared to a 89 GHz based SIC. Consequently, this resolution-enhanced SIC technique can potentially initialise higher-resolution coupled ocean and sea ice forecasting systems through data assimilation.

A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing.

Global Mapping of Microwave Emissivity and Emitting Depth in Arid Areas Using GMI Observations

AbstractOver arid areas, observations of brightness temperatures by passive microwave radiometers are affected by the variation of the emitting depth with wavelengths. When this variation is unaccounted for, it limits the assimilation of passive microwaves over deserts in Numerical Weather Prediction models and it causes large errors in passive microwave retrievals of land surface temperatures. The emitting depths, along with the corresponding emissivities, are estimated from 10 to 89 GHz, using the non‐Sun‐synchronous observations of the Global Precipitation Mission Microwave Imager to reconstruct the monthly diurnal cycle of brightness temperature. The soil temperature profile is modeled using a two‐term Fourier decomposition based on the ERA5 surface temperature. The combination of the observation and the modeled temperature allows for an estimation of the microwave effective emitting depth. The emitting depth is estimated to be up to 25 cm at 36 GHz, resulting in large differences between the surface temperature and the effective emitting temperature. The variation of emitting depth with frequency is parameterized, and a companion data set provides the necessary parameters to calculate the emitting depth for arid areas between 10 and 89 GHz, globally. The benefit of this parameterization is quantified, with an application to the modeling of observations from the Special Sensor Microwave Imager Sounder over arid areas.

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.

Reconstruct the AMSR-E/2 thin ice thickness algorithm to create a long-term time series of sea-ice production in Antarctic coastal polynyas

This study presented an extended time series of ice production in Antarctic coastal polynyas for 20 years of 2002–21 using high spatial resolution satellite data from passive microwave sensors: the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). For this, we reconstructed the previously presented thin ice thickness algorithms and re-estimated ice production by replacing atmospheric input data for the heat flux calculations with the newly released ECMWF Reanalysis v5 (ERA5). The consistency of ice production between AMSR-E and AMSR2, whose operation periods do not overlap, was confirmed from comparisons with ice production by the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) with relatively coarse spatial resolution. The open ocean area, which cannot be detected from the thin ice thickness algorithm, was defined using sea-ice concentration (SIC) from AMSR-E and AMSR2. The satellite derived SIC has been suggested to be underestimated in the new thin ice (polynya) area. The effect of the underestimation on the ice production estimation was evaluated. The underestimation of SIC from three major algorithms was also investigated from comparisons with the thin ice thickness presented in this study. The coastal polynya dataset covering 20 years with the higher spatial resolution presented in this study is vital for climate-change-related studies.

Improved algorithm for determining the freeze onset of Arctic sea ice using AMSR-E/2 data

Over the past several decades, Arctic sea ice has experienced a significantly longer summer melt season, with an earlier melt onset and delayed freeze onset. The early melt onset/melt onset date (EMO/MO) and early freeze onset/freeze onset date (EFO/FO) products developed using the passive microwave (PMW) algorithm derived from Special Sensor Microwave/Imager (SSM/I) passive microwave data are widely used in the study of the variability in Arctic sea ice freezing melting. Here, we apply the PMW algorithm to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E/2) dataset at a higher spatial resolution and improve the algorithm. We first limit the start and end dates according to the sea ice concentration (SIC). Then, we exclude false freezing signals by identifying high variability in SIC; finally, we consider changes in the distribution of sea ice types (first-year ice and multiyear ice) over time to obtain an improved algorithm. The early freeze onset (EFO) and FO derived from the AMSR data are then compared with results derived from the surface air temperature and sea skin temperature (SAT & SKT) and SSM/I products. The spatial distribution of the new EFO results shows greater consistency with SAT & SKT than with the results of SSM/I, with a mean error, absolute error, and root mean square error for the SAT & SKT comparison of 3.7 days, 10.4 days, and 16.0 days, respectively, for the AMSR2 period. The improved FO and EFO correct the small values caused by misidentified summer melt signals and the large values caused by sea ice dynamic processes after the melt season. There is a constant change in ice type over 20%–30% of the ice cover, and the improved FO and EFO correct for anomalies due to misidentified ice types. The improved results correlate better with the interannual variability in EFO derived from SAT & SKT in subregions, especially in ice marginal regions. The Arctic sea ice EFO north of 70°N was delayed at a rate of 6.4 day/dec from 2003 to 2021, with remarkable trends found in the Laptev (14.6 day/dec) and Kara (14.1 day/dec) Seas.

A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors

Abstract. Sea ice concentration (SIC) is the main geophysical variable for quantifying change in sea ice in the polar regions. A continuous SIC product is key to informing climate and ecosystem studies in the polar regions. Our study generates a new SIC product covering the Arctic and Antarctic from November 2010 to December 2019. It is the first long-term SIC product derived from the Microwave Radiation Imager (MWRI) sensors on board the Chinese FengYun-3B, FengYun-3C, and FengYun-3D satellites, after a recent re-calibration of brightness temperature. We modified the previous Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie point algorithm mainly by changing input brightness temperature and initial tie points. The MWRI-ASI SIC was compared to the existing ASI SIC products and validated using ship-based SIC observations. Results show that the MWRI-ASI SIC mostly coincides with the ASI SIC obtained from the Special Sensor Microwave Imager series sensors, with overall biases of −1 ± 2 % in the Arctic and 0.5 ± 2 % in the Antarctic, respectively. The overall mean absolute deviation between the MWRI-ASI SIC and ship-based SIC is 16 % and 17 % in the Arctic and Antarctic, respectively, which is close to the existing ASI SIC products. The trend of sea ice extent (SIE) derived from the MWRI-ASI SIC closely agrees with the trends of the Sea Ice Index SIEs provided by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) and the National Snow and Ice Data Center (NSIDC). Therefore, the MWRI-ASI SIC is comparable with other SIC products and may be applied alternatively. The MWRI-ASI SIC dataset is available at https://doi.org/10.1594/PANGAEA.945188 (Chen et al., 2022b).

A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range

Abstract. We present a long-term data set of 1∘×1∘ monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. In comparison to the retrieval algorithm of Borger et al. (2020), several modifications and filters have been applied accounting for instrumental issues (such as OMI's “row anomaly”) or the inferior quality of solar reference spectra. For instance, to overcome issues related to low-quality reference spectra, the daily solar irradiance spectrum is replaced by an annually varying mean earthshine radiance obtained in December over Antarctica. For the TCWV data set, we only consider measurements with an effective cloud fraction less than 20 %, an air mass factor (AMF) greater than 0.1, a snow- and ice-free ground pixel, and an OMI row that is not affected by the row anomaly over the complete time range of the data set. The individual TCWV measurements are then gridded to a regular 1∘×1∘ lattice, from which the monthly means are calculated. The investigation of sampling errors in the OMI TCWV data set shows that these are dominated by the clear-sky bias and cause on average deviations of around −10 %, which is consistent with the findings of previous studies. However, the spatiotemporal sampling errors and those due to the row-anomaly filter are negligible. In a comprehensive intercomparison study, we demonstrate that the OMI TCWV data set is in good agreement with the global reference data sets of ERA5 (fifth-generation ECMWF atmospheric reanalysis), RSS SSM/I (Remote Sensing Systems Special Sensor Microwave Imager), and CM SAF/CCI TCWV-global (COMBI): over ocean the orthogonal distance regressions indicate slopes close to unity with very small offsets and high coefficients of determination of around 0.96. However, over land, distinctive positive deviations of more than +10 kg m−2 are obtained for high TCWV values. These overestimations are mainly due to extreme overestimations of high TCWV values in the tropics, likely caused by uncertainties in the retrieval input data (surface albedo, cloud information) due to frequent cloud contamination in these regions. Similar results are found from intercomparisons with in situ radiosonde measurements from version 2 of the Integrated Global Radiosonde Archive (IGRA2) data set. Nevertheless, for TCWV values smaller than 25 kg m−2, the OMI TCWV data set shows very good agreement with the global reference data sets. Furthermore, a temporal stability analysis proves that the OMI TCWV data set is consistent with the temporal changes in the reference data sets and shows no significant deviation trends. As the TCWV retrieval can be easily applied to further satellite missions, additional TCWV data sets can be created from past missions, such as the Global Ozone Monitoring Experiment-1 (GOME-1) or the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY); under consideration of systematic differences (e.g. due to different observation times), these data sets can be combined with the OMI TCWV data set in order to create a data record that would cover a time span from 1995 to the present. Moreover, the TCWV retrieval will also work for all missions dedicated to NO2 in the future, such as Sentinel-5 on MetOp-SG. The Max Planck Institute for Chemistry (MPIC) OMI total column water vapour (TCWV) climate data record (CDR) is available at https://doi.org/10.5281/zenodo.7973889 (Borger et al., 2023).

Spectral Linear Mixing Model application in passive microwave data to analyze Antarctic surface melting dynamics (1978-2018).

Several studies have utilized passive microwave imagery for monitoring snowmelt in Antarctica. However, due to the low spatial resolution of these images (25 km), the quantification of snowmelt is not precise. To enhance the accuracy of these estimations, this study proposed a subpixel analysis approach based on a Spectral Linear Mixing Model. This approach was applied to images obtained from channels 18/19 GHz and 37 GHz, both horizontally and vertically polarized, acquired from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSM/IS) instruments, spanning the period 1978-2018. The spatiotemporal analysis of the estimated snowmelt fraction images indicated that the most persistent and intensive melt was observed on the Antarctic Peninsula, particularly on the Larsen, Wilkins, George VI, and Wordie ice shelves. The melting period in the Antarctic Peninsula began in late October, with a peak in early January, and ended in late March. Other regions with persistent and intensive snowmelt were Mary Bird Land and Wilkes Land, followed by Dronning Maud Land, Amery Ice Shelf, Filchner-Ronne Ice Shelf, and Ross Ice Shelf. These snowmelt data are valuable for modeling the impacts of snowmelt on glacial systems, local coastal environments, and sea-level rise.

Remote Sensing of Surface Melt on Antarctica: Opportunities and Challenges

Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four frequently used remote sensing sensors: two active microwave sensors, ASCAT (Advanced Scatterometer) and Sentinel-1, a passive microwave sensor SSMIS (Special Sensor Microwave Imager/Sounder), and an optical sensor MODIS (Moderate Resolution Imaging Spectroradiometer). We intercompare the melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. We discuss that discrepancies between sensors are mainly due to (1) cloud obstruction and polar darkness, (2) frequency-dependent penetration of satellite signals, (3) temporal resolution, and (4) spatial resolution, as well as (5) the applied melt detection methods. Nevertheless, we argue that different sensors can complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

A machine learning-based dynamic ensemble selection algorithm for microwave retrieval of surface soil freeze/thaw: A case study across China

ABSTRACT The surface soil freeze/thaw (FT) cycle serves as a “switch” for land surface processes; accurate retrieval of surface FT dynamics based on satellite passive microwave remote sensing is critical for studies on climate change and dynamics of the cryosphere. This study aims to improve FT retrieval accuracy by developing a new FT retrieval algorithm that applies the K-Nearest Oracle Union (KNORA-UNION) dynamic ensemble selection algorithm. This algorithm can optimally integrate three machine learning models on a grid cell scale, namely Random Forests, Extra-Trees, and Extreme Gradient Boosting. We applied our developed freeze/thaw dynamic ensemble selection retrieval algorithm (FT-DESA) to retrieve China’s daily surface FT states from 2009 to 2020 based on multiband Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperatures. We then evaluated our FT-DESA results by comparing the observations of 2398 stations and three other existing FT algorithms, including the modified seasonal threshold algorithm (MSTA), decision tree algorithm (DTA), and dual-index algorithm (DIA) across China. Our results show that FT-DESA has the highest retrieval accuracy and the lowest biases across China among the four algorithms. The mean classification accuracy for the PM and AM overpasses of FT-DESA is 89% and 84%, respectively. The evaluations further indicate that some of the existing algorithms do not reflect the temporal and spatial heterogeneity in selecting thresholds for FT classification. This study demonstrates that the freeze/thaw dynamic ensemble selection algorithm can provide daily estimates of surface FT states across China, improve FT states’ retrieval accuracy, and provide a valuable multi-decadal record for daily FT states.

Validation of IMERG Oceanic Precipitation over Kwajalein

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.

A Retrospective Satellite Analysis of the June 2012 North American Derecho

The North American Derecho of 29–30 June 2012 exhibits many classic progressive and serial derecho features. It remains one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the Mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar-orbiting satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS), and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. Furthermore, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS.

A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere

Abstract. Seasonal ice cover is one of the important attributes of lakes in middle- and high-latitude regions. The annual freeze-up and breakup dates as well as the duration of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate; hence, they can be used as an indicator of climate variability and change. In addition to optical, active microwave, and raw passive microwave data that can provide daily observations, the Calibrated Enhanced-Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate source of passive microwave brightness temperature (TB) measurements for the determination of lake ice phenology on a 3.125 km grid. This study used Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) data from the CETB dataset to extract the ice phenology for 56 lakes across the Northern Hemisphere from 1979 to 2019. According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. When ice phenology could be extracted from more than one satellite over overlapping periods, results from the satellite offering the largest number of observations were prioritized. The lake ice phenology results showed strong agreement with an existing product derived from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2002 to 2015), with mean absolute errors of ice dates ranging from 2 to 4 d. Compared with near-shore in situ observations, the lake ice results, while different in terms of spatial coverage, still showed overall consistency. The produced lake ice record also displayed significant consistency when compared to a historical record of annual maximum ice cover of the Laurentian Great Lakes of North America. From 1979 to 2019, the average complete freezing duration and ice cover duration for lakes forming a complete ice cover on an annual basis were 153 and 161 d, respectively. The lake ice phenology dataset – a new climate data record (CDR) – will provide valuable information to the user community about the changing ice cover of lakes over the last 4 decades. The dataset is available at https://doi.org/10.1594/PANGAEA.937904 (Cai et al., 2021).

Mitigation of Systematic Noise in F16 SSMIS LAS Channels Observations for Tropical Cyclone Applications

The Special Sensor Microwave Imager Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F16, launched on 18 October 2003, was the first conical-scanning radiometer to combine the Special Sensor Microwave/Imagers (SSM/I), Special Sensor Microwave/Temperature Sounder (SSM/T), and the Special Sensor Microwave/Water Vapor Sounder (SSM/T2). Nearly 20 years of F16 SSMIS data are available to the general public, providing many opportunities to study the atmosphere at both the synoptic and decadal scales. However, data noise from complicated structures has occurred in the brightness temperature (TB) observations of lower atmospheric sounding (LAS) channels since 25 April 2013. We used a two-dimensional Fast Fourier Transform to analyze the characteristic features of data noise in cross-track and along-track directions. We found that the data noise is around 1–2 K and occurs at certain cross-track wavelengths (Δλ)noise. A latitudinal variation was found for (Δλ)noise. Due to noise interference, TB observations reflecting rain, clouds, tropical cyclone warm core, temperature, and water vapor distributions are not readily distinguishable, especially in channels above the middle troposphere (channels 4–7 and 24), whose dynamic TB range is smaller than low tropospheric channels 1–3. Examples are provided to show the impact of the proposed noise mitigation for conical-scanning TB observations to capture 3D structures of hurricanes directly. Once the noise in F16 SSMIS LAS channels from 25 April 2013to the present is eliminated, we may investigate the decadal change of many features of tropical cyclones derivable from these TB observations.

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.

Mitigation of Significant Data Noise in F17 SSMIS Observations since October 2017

Special Sensor Microwave Imager Sounder (SSMIS) temperature sounding observations have been made available since the launch of the Defense Meteorological Satellite Program (DMSP), F16, on 18 October 2003. These conical-scanning observations of brightness temperature are ideal for investigating long-term structural changes in tropical cyclones throughout the globe. The SSMIS temperature sounding data started to contain significant across-track high-frequency striping noise spikes at a frequency of 0.14 s−1 starting on 20 October 2017 for F17. A Fast Fourier Transform (FFT) was used to remove the noise in channels 1–7 and 24. The across-track striping noise is most significant for the four channels of the lowest peak weighting functions. The data noise is as large as 15 K for channels 1–3, 2.5 K for channel 4, 0.5 K for channels 5–7, and 0.75 K for channel 24. We found some remaining along-track striping noise around 0.5 K in channels 2–4, which is removed by employing a principal component analysis and an ensemble empirical mode decomposition combined method. An advantage for conical-scanning observations of brightness temperature to directly capture typhoon structures is then illustrated. Although buried under the data noise in the original data, the structural features of typhoon Lekima (2019) could clearly be seen after the noise mitigation. Lekima reached a typhoon intensity on 7 August 2019 and its center was characterized by a warm anomaly of more than 7 K around 200 hPa (channel 5) and a cold center of less than −6 K around 945 hPa (channel 2). This study prepares us for using satellite observations to understand the effect of climate change on tropical cyclone intensity and rain-band structures over the past two decades.