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The two-LAyered snow Radiative Transfer (LART) model has been proposed for snow remote sensing applications. It is based on analytical approximations of the radiative transfer theory. The geometrical optics approximation has been used to derive the local snow optical parameters, such as the probability of photon absorption by ice grains and the average cosine of single light scattering in a given direction in a snowpack. The application of the model to the selected area in Antarctica has shown that the technique is capable of retrieving the snow grain size both in the upper and lower snow layers, with grains larger in the lower snow layer as one might expect due to the metamorphism processes. Such a conclusion is confirmed by ground measurements of the vertical snow grain size variability in Antarctica. [ABSTRACT FROM AUTHOR]

The two-LAyered snow Radiative Transfer (LART) model has been proposed for snow remote sensing applications. It is based on analytical approximations of the radiative transfer theory. The geometrical optics approximation has been used to derive the local snow optical parameters, such as the probability of photon absorption by ice grains and the average cosine of single light scattering in a given direction in a snowpack. The application of the model to the selected area in Antarctica has shown that the technique is capable of retrieving the snow grain size both in the upper and lower snow layers, with grains larger in the lower snow layer as one might expect due to the metamorphism processes. Such a conclusion is confirmed by ground measurements of the vertical snow grain size variability in Antarctica.

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data. [ABSTRACT FROM AUTHOR]

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data.

Despite significant progress made in snowfall estimation from space, methods utilizing passive microwave measurements continue to be plagued by low detectability compared to those that estimate rainfall. This article presents a hybrid snowfall detection algorithm that combines the output from a statistical algorithm utilizing satellite passive microwave measurements with the output from a statistical algorithm trained with in situ data that uses meteorological variables derived from a global forecast model as predictors. The satellite algorithm computes the probability of snowfall over land using logistic regression and the principal components of the high‐frequency brightness‐temperature measurements at AMSU/MHS and ATMS channel frequencies 89 GHz and above. In a separate investigation, analysis of modelled data derived from NOAA's Global Forecast System (GFS) showed that cloud thickness and relative humidity at 1 to 3 km height were the best predictors of snowfall occurrence. A statistical logistical regression model that combined cloud thickness, relative humidity and vertical velocity was selected among statistically significant variants as the one with the highest overall classification accuracy. Next, the weather‐based and satellite model outputs were combined in a weighting scheme to produce a final probability of snowfall output, which was then used to classify a weather event as "snowing" or "not snowing" based on an a priori threshold probability. Statistical analysis indicated that a scheme with equal weights applied to the weather‐based and satellite model significantly improved satellite snowfall detection. Example applications of the hybrid algorithm over continental USA demonstrated the improvement for a major snowfall event and for an event dominated by lighter snowfall. The article presents a hybrid approach to satellite snowfall detection that can improve the performance of satellite‐based methods and increase their utility in operational weather and hydrological forecasting. It presents an analysis and new insights into modelled meteorological variables that are related to snowfall occurrence, and a weather‐based snowfall detection algorithm using modelled data from a global weather forecast system. [ABSTRACT FROM AUTHOR]

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 DTB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 DTB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg.m-2, IWP > 0.24 kg.m-2 over land, and SIC > 57%, TPW > 5.1 kg.m-2 over sea). The complex combined 166 DTB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms.

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI. [ABSTRACT FROM AUTHOR]