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The Antarctic ice sheet, Earth’s largest ice mass, is vital to the global climate system. Analyzing its thermal behavior is crucial for sea-level projections and ice shelf assessments; however, internal temperature studies remain challenging due to the harsh environment and limited access to the site. Using ten years of Soil Moisture Active Passive (SMAP) satellite passive microwave brightness temperature (TB) data (2015–2025), we examined changes in TB across Antarctica. Results show a stronger warming trend in West Antarctica, with TB increasing by over 1.5 K over a decade, while East Antarctica remains relatively stable, showing only seasonal summer warming and winter cooling. Furthermore, TB in the Antarctic region correlates best with internal temperatures at depths of 500–2000 m, as indicated by the effective soil temperature, as demonstrated by the modeling data and the τ-z model’s inference. However, the total enthalpy is inconsistent with the TB trend and exhibits the opposite effect when combined with the sensing depth. By comparing the weak trend in surface ice temperature changes, we conclude that the TB warming trend observed on the western side of the Antarctic over the past decade does not originate from the increasing temperatures within the internal ice shelves, which differs from the increase in temperatures at the Antarctic margins.

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

This study examines the impacts of assimilating Soil Moisture Active Passive (SMAP) L-band brightness temperatures (TBs) on warm season short-range numerical weather prediction (NWP) forecasts. Focusing upon the summer 2015 period over North America, offline assimilation cycles are run with the Canadian Land Data Assimilation System (CaLDAS) to compare the impacts of assimilating SMAP TB versus screen-level observations to analyze soil moisture. The analyzed soil moistures are quantitatively compared against a set of in situ sparse soil moisture networks and a set of SMAP core validation sites. These surface analyses are used to initialize a series of 48-h forecasts where near-surface temperature and precipitation are evaluated against in situ observations. Assimilation of SMAP TBs leads to soil moisture that is markedly improved in terms of correlation and standard deviation of the errors (STDE) compared to the use of screen-level observations. NWP forecasts initialized with SMAP-derived soil moistures exhibit a general dry bias in 2-m dewpoint temperatures (TD2m), while displaying a relative warm bias in 2-m temperatures (TT2m), when compared to those forecasts initialized with soil moistures analyzed with screen-level temperature errors. Largest impacts with SMAP are seen for TD2m, where the use of screen-level observations leads to a daytime wet bias that is reduced with SMAP. The overall drier soil moisture leads to improved precipitation bias scores with SMAP. A notable deterioration in TD2m STDE scores was found in the SMAP experiments during the daytime over the Northern Great Plains. A reduction in the daytime TD2m wet bias was found when the observation errors for the screen-level observations were increased.

The relationship between tropical cyclone (TC) convective characteristics and TC intensity change is explored using infrared and passive microwave satellite imagery of TCs in the North Atlantic and eastern North Pacific basins from 1989 to 2016. TC intensity change episodes were placed into one of four groups: rapid intensification (RI), slow intensification (SI), neutral (N), and weakening (W). To account for differences in the distributions of TC intensity among the intensity change groups, a normalization technique is introduced, which allows for the analysis of anomalous TC convective characteristics and their relationship to TC intensity change. A composite analysis of normalized convective parameters shows anomalously cold infrared and 85-GHz brightness temperatures, as well as anomalously warm 37-GHz brightness temperatures, in the upshear quadrants of the TC are associated with increased rates of TC intensification, including RI. For RI episodes in the North Atlantic basin, an increase in anomalous liquid hydrometeor content precedes anomalous ice hydrometeor content by approximately 12 h, suggesting convection deep enough to produce robust ice scattering is a symptom of, rather than a precursor to, RI. In the eastern North Pacific basin, the amount of anomalous liquid and ice hydrometeors increases in tandem near the onset of RI. Normalized infrared and passive microwave brightness temperatures can be utilized to skillfully predict episodes of RI, as the forecast skill of RI episodes using solely normalized convective parameters is comparable to the forecast skill of RI episodes by current operational statistical models.

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.

This paper presents a general approach to quantify absorption model uncertainty due to uncertainty in the underlying spectroscopic parameters. The approach is applied to a widely used microwave absorption model (Rosenkranz, 2017) and radiative transfer calculations in the 20–60 GHz range, which are commonly exploited for atmospheric sounding by microwave radiometer (MWR). The approach, however, is not limited to any frequency range, observing geometry, or particular instrument. In the considered frequency range, relevant uncertainties come from water vapor and oxygen spectroscopic parameters. The uncertainty of the following parameters is found to dominate: (for water vapor) self- and foreign-continuum absorption coefficients, line broadening by dry air, line intensity, the temperature-dependence exponent for foreign-continuum absorption, and the line shift-to-broadening ratio; (for oxygen) line intensity, line broadening by dry air, line mixing, the temperature-dependence exponent for broadening, zero-frequency line broadening in air, and the temperature-dependence coefficient for line mixing. The full uncertainty covariance matrix is then computed for the set of spectroscopic parameters with significant impact. The impact of the spectroscopic parameter uncertainty covariance matrix on simulated downwelling microwave brightness temperatures (TB) in the 20–60 GHz range is calculated for six atmospheric climatology conditions. The uncertainty contribution to simulated TB ranges from 0.30 K (subarctic winter) to 0.92 K (tropical) at 22.2 GHz and from 2.73 K (tropical) to 3.31 K (subarctic winter) at 52.28 GHz. The uncertainty contribution is nearly zero at 55–60 GHz frequencies. Finally, the impact of spectroscopic parameter uncertainty on ground-based MWR retrievals of temperature and humidity profiles is discussed.

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection.

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.

GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites.