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Recently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles. Plain Language Summary: This study used advanced satellite observations and land surface models to learn more about the water and carbon cycles and their interactions. The soil hydraulic conductivity at saturation (Ks) and the parameter for the water retention curve were identified as important. They affect how fast water can move in soil and how much water can be held by soil, which affects the available water in the soil for plants to use and makes them particularly important in water and carbon cycle simulation in dry areas. Two vegetation parameters affecting photosynthesis can help to improve the carbon cycle simulation but cannot change the soil moisture much because of the offline model limitation. The adjustment on the two soil property parameters and the two vegetation parameters improved the global water cycle simulation by 15% and the carbon cycle simulation by 12%. The introduction of dynamic vegetation processes into our model improved the carbon cycle simulation by 12% globally and helped to better understand the water and carbon cycles and their interactions. Key Points: Key processes and parameters are identified using satellite data for advances in understanding carbon and water cycles and interactionsThe soil hydraulic properties, wilting point, and maximum Rubisco carboxylation rate are key parameters linking water‐carbon cyclesThe dynamic vegetation process plays an important role in water and carbon cycles [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) account for more than 90% of the total meridional water vapor flux in midlatitudes, and 25-50% of the annual precipitation in the coastal western United States. In this study, reflectivity profiles from the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) are used to evaluate precipitation and temperature characteristics of ARs over the western coast of North America and the eastern North Pacific Ocean. Evaluation of GPM-DPR bright-band height using a network of ground-based vertically pointing radars along the West Coast demonstrated exceptional agreement, and comparison with freezing level height from reanalyses over the eastern North Pacific Ocean also consistently agreed, indicating that GPM-DPR can be used to independently validate freezing level in models. However, precipitation comparison with gridded observations across the western United States indicated deficiencies in GPM-DPR's ability to reproduce the spatial distribution of winter precipitation, likely related to sampling frequency. Over the geographically homogeneous oceanic portion of the domain, sampling frequency was not problematic, and significant differences in the frequency and intensity of precipitation between GPM-DPR and reanalyses highlighted biases in both satellite-observed and modeled AR precipitation. Reanalyses precipitation rates below the minimum sensitivity of GPM-DPR accounted for a 20% increase in total precipitation, and 25% of radar-derived precipitation rates were greater than the 99th percentile precipitation rate in reanalyses. Due to differences in the proportions of precipitation in convective, stratiform bright-band, and non-bright-band conditions, AR conditions contributed nearly 10% more to total precipitation in GPM-DPR than reanalyses. [ABSTRACT FROM AUTHOR]

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay-the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm. [ABSTRACT FROM AUTHOR]