The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring.

Grasslands, covering over 40% of terrestrial land surfaces, play a critical role in regional water cycling through their greening processes. However, the decoupling mechanisms between grassland greening and water availability (WA) changes across the Northern Hemisphere, along with their future trajectories, remain poorly understood. Here, we integrated multi-source satellite observations with CMIP6 model ensembles to systematically assess the spatiotemporal evolution and trend divergence of leaf area index (LAI) and WA across Northern Hemisphere grasslands from 2000 to 2100. Our results showed that grassland LAI exhibited sustained growth during 2000–2020, with 55.28% of regions showing significant increasing trends. However, 73.67% of grassland regions experienced declining WA during the historical period, revealing widespread decoupling between grassland greening and water deficit. Future scenario projections indicated a reversal to increasing WA trends, with 57.51% of regions showing significant increases under SSP5–8.5. Furthermore, 61.87% of grasslands exhibited greening-driven drying (GDD) characteristics during the historical period, while greening-driven wetting (GDW) regions were projected to expand to 72.44% in the future. Analysis along aridity gradients revealed that humid zones contributed most prominently to LAI and WA changes. Mechanistic decomposition demonstrated that grassland WA changes shifted from precipitation-dominated control (53.60%) in the historical period toward a regime jointly governed by precipitation dominance and coupled precipitation–evapotranspiration drivers in the future. Concurrently, the dominant factor controlling grassland greening transitioned from vapor-pressure deficit (VPD) to temperature (TEM) control. Additionally, driving factors exhibited pronounced differentiation patterns along aridity gradients during the historical phase: arid zones were dominated by soil moisture (SM) and semi-arid zones displayed dual control by SM and VPD, while humid zones were governed by coupled TEM-VPD regulation. This study reveals the divergent trends between grassland greening and WA and unravels their driving mechanisms, offering important scientific evidence for formulating regionally differentiated ecological water resource management strategies.

Photovoltaic (PV) development in arid regions faces challenges such as sparse observational data, insufficient consideration of natural environmental heterogeneity, and a disconnect between site suitability assessments and actual power generation potential. To address these issues, this study integrates ERA5-Land reanalysis data, ESA CCI land cover data, DEM terrain data, and PV site information to construct a land suitability factor ranging from 0 to 1. Coupled with the Photovoltaic Library Python model(PVLIB-Python), a comprehensive assessment framework is established, spanning from site suitability to power generation potential and emission reduction benefits.Results show that Xinjiang’s theoretical PV generation potential from 2015 to 2025 averages approximately 113.5 PWh per year. After applying land suitability constraints, the technical potential decreases to 71.4 PWh annually, representing about 63% of the theoretical potential. Spatially, suitability follows a pattern of “concentration in basins and dispersion in mountainous areas,” with highly suitable zones mainly located in the central-western Tarim Basin, the Hami Basin, and the southern edge of the Junggar Basin.Based on the calculated technical potential, annual PV deployment could achieve around 53.5 billion tonnes of CO₂ emission reductions.Incorporating environmental benefits significantly lowers the levelized cost of energy (LCOE) for PV systems, demonstrating considerable net social value. This study provides quantitative evidence to support the scientific planning of PV power stations in Xinjiang and the formulation of carbon neutrality pathways.

Accurately characterizing variation in public transport demand is important for urban traffic management, especially when weather conditions and public-health disruptions jointly reshape travel patterns. Using monthly transportation statistics and weather observations for Nanjing, China, from February 2019 to December 2022, this study examines associations between weather conditions and passenger flow across buses, taxis, and the Rail Transit System (RTS) during the COVID-19 period. Random Forest (RF) and Shapley Additive Explanations (SHAP)-based interpretation were used as exploratory tools to examine the relative associations of weather variables with passenger-flow variation. The results show that RTS remained the dominant travel mode in Nanjing, followed by buses and taxis, and that passenger flow for all three modes declined sharply at the onset of the pandemic before gradually recovering. During the study period, temperature generally increased, whereas other weather variables showed changes in varying directions. Although the importance rankings derived from the RF and SHAP methods are not entirely consistent, both identify temperature and precipitation as important weather factors influencing changes in traffic demand. In addition, the pandemic was associated with a marked shift in modal structure: before the pandemic, passenger-flow shares were relatively balanced across modes, whereas afterward taxi and RTS accounted for a larger share and bus ridership declined. These findings provide may offer a preliminary reference for adaptive transport planning.