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Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods

High- and low-latitude climatic processes in the North Pacific Ocean are important components of the global climate system. For example, the interplay among North Pacific atmospheric circulation, ocean circulation, and biological productivity affects atmospheric carbon dioxide levels and marine oxygen concentrations. Here we review recent research on the North Pacific paleoclimatic and paleoceanog

The role of multiple groups of primary biological aerosol particles (PBAPs) as ice nucleating particles (INPs), and of ice formation processes such as time-dependent freezing of various INPs, and various secondary ice production(SIP) mechanisms in overall ice concentration has been evaluated in a range of cloud systems by simulating them numerically with the state-of-the-art ‘Aerosol-Cloud’ (AC) m

Cold-season methane (CH4) emissions may be poorly constrained in wetland models. We examined cold-season CH4 emissions simulated by 16 models participating in the Global Carbon Project model intercomparison and analyzed temporal and spatial patterns in simulation results using prescribed inundation data for 2000–2020. Estimated annual CH4 emissions from northern (>60°N) wetlands averaged 10.0 ± 5.

The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian op

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice-water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Diff

Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China span

Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely

In this paper we investigate simultaneous radial distortion calibration and motion estimation for vehicles travelling parallel to planar surfaces. This is done by estimating the inter-image homography between two poses, as well as the distortion parameter. Radial distortion correction is often performed as a pre-calibration step; however, accurately estimating the distortion profile without specia

We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based

Tropical cloud forests are characterized by abundant and biodiverse mosses which grow epiphytically as well as on the ground. Nitrogen (N)-fixing cyanobacteria live in association with most mosses, and contribute greatly to the N pool via biological nitrogen fixation (BNF). However, the availability of nutrients, especially N and phosphorus (P), can influence BNF rates drastically. To evaluate the

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. Methods: We define the strength of local voxel-to

Remote sensing is an effective means for lake water level monitoring on the Tibetan Plateau (TP). The purpose of this study is to estimate water levels of lakes on the TP using the Global Ecosystem Dynamics Investigation (GEDI) and Cloud and Land Elevation Satellite-2 (ICESat-2), evaluate the performance of ICESat-2 and GEDI in estimating water levels, and analyze the differences of water level ob

Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E

Clouds are essential elements within Earth's atmosphere, posing a challenge for cloud-resolving models in understanding the creation of new cloud ice particles from existing ice and liquid phases. Such ice initiation determines cloud microphysical and radiative properties, influencing cloud phase, precipitation and cloud extent/properties. To address this challenge effectively, it proves beneficia

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrolo

We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and prop

Despite the fact that the response of tropical hydroclimate to North Atlantic cooling events during the Heinrich Stadial 1 (HS1) has been extensively studied in African, South American and Indonesia, the nature of such responses remains debated. Here we investigate the tropical hydroclimate pattern over the Indo-Asian-Australian monsoon region during the HS1 by integrating hydroclimatic records, a

Modern embedded software includes complex functionalities and routines, often implemented by splitting the code across different tasks. Such tasks communicate their partial computations to their successors, forming a task chain. Traditionally, this architecture relies on the assumption of hard deadlines and timely communication. However, in actual implementations, tasks may miss their deadlines, t