Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method

High-resolution daily precipitation estimation data derived from Wuhan University Satellite and Gauge precipitation Collaborated Correction method (WHU-SGCC) in TIFF format

A daily precipitation estimation derived from WHU-SGCC method blending daily precipitation gauge data, gridded precipitation data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over Jinsha River Basin in summer 2016

Improving the Climate Hazards Group Infrared Precipitation (CHIRP) using WHU-SGCC method over the Jinsha River Basin from 1990 to 2014

A daily precipitation estimation derived from WHU-SGCC method (Wuhan University Satellite and Gauge precipitation Collaborated Correction), blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin during the different seasons from 1990 to 2014

High-resolution daily precipitation estimation data from WHU-SGCC method: A novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River Basin

A daily precipitation estimation derived from WHU-SGCC method (Wuhan University Satellite and Gauge precipitation Collaborated Correction), blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin during summer seasons from 1990 to 2014

Forest Height Inversion Based on Time–Frequency RVoG Model Using Single-Baseline L-Band Sublook-InSAR Data

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor

Improving the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) using BCFS method based on the precipitation feature space over the Han River Basin from 1998 to 2019

Accurate and reliable high-resolution spatial precipitation data are crucial for hydrometeorology research. But most of the precipitation products have significant differences in terms of estimation accuracy owning to the influence of sensors, climate and terrain. Moreover, due to the neglect of the precipitation feature and the sparse distribution of gauge stations, the existing bias correction methods often have great uncertainties under different precipitation intensities. Thus, we developed a Daily Precipitation Bias Correction Approach Based on Feature Space Construction and Gauge-Satellite Fusion (BCFS). First, the precipitation feature space under different precipitation intensities was reconstructed, considering the attribute similarities of the spatial values, non-spatial values and trends. Then, the numerical relationships of correlated neighboring pixels were established taking account of these three similarities. Finally, the effective correction of the daily precipitation bias based on a small number of stations and a great number of pixels was achieved by the integration methods of variational mode decomposition, multivariate random forest regression model, and the spatial interpolation method. Using gauge station observations and the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) (1998-2019) and taking the Han River basin (China) as a case study, we quantitatively analyzed the accuracy of the bias correction results comparing the BCFS with the original CHIRPS precipitation estimations and the Wuhan University Satellite and Gauge precipitation Collaborated Correction method (WHU-SGCC). The results demonstrated the BCFS can effectively improve the estimation accuracy under different daily precipitation intensities. Therefore, the method is meaningful to make up for the deficiency of satellite-based estimations and provide high-precision daily precipitation for hydrometeorological and environmental monitoring and forecasting

Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method

Undrained Cyclic Response and Resistance of Saturated Calcareous Sand considering Initial Static Shear Effect

Sand elements in the natural or manmade field have often undergone initial static shear stresses before suffering cyclic loading. To explore the effect of static shear stress, a series of undrained cyclic triaxial tests were performed on dense and loose calcareous sand under different initial and cyclic shear stresses. The triaxial test results are used to describe the effect of static shear stress on the cyclic response of the calcareous sand with different relative density. Cyclic mobility, flow deformation, and residual deformation accumulation are the three main failure modes under varying static and cyclic shear stress levels. The cyclic resistance of dense sand is greater than that of loose sand, but the initial static stress has different effects on the cyclic resistance of the two kinds of sand. The dense sand owns a higher cyclic resistance with SSR increasing, while for the loose sand, 0.12 is the critical SSR corresponding to the lowest value of the cyclic resistance. The dense sand has more fast accumulation of dissipated energy, compared with loose sand. Additionally, an exponential relationship is established between static shear stress, relative density, and normalized energy density