A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling

Abstract

Studies of land surface processes in complex terrain often require estimates of meteorological variables, i.e., the incoming solar irradiance (Qsi), to force land surface models. However, estimates of Qsi are rarely evaluated within mountainous environments. We evaluated four methods of estimating Qsi: the CERES Synoptic Radiative Fluxes and Clouds (SYN) product, MTCLIM, a regional reanalysis product derived from a long‐term Weather Research and Forecast simulation, and Mountain Microclimate Simulation Model (MTCLIM). These products are evaluated over the Central Valley and Sierra Nevada mountains in California, a region with meteorology strongly impacted by complex topography. We used a spatially dense network of Qsi observations (n = 70) to characterize the spatial characteristics of Qsi uncertainty. Observation sites were grouped into five subregions, and Qsi estimates were evaluated against observations in each subregion. Large monthly biases (up to 80 W m−2) outside the observational uncertainty were found for all estimates in all subregions examined, typically reaching a maximum in the spring. We found that MTCLIM and SYN generally perform the best across all subregions. Differences between Qsi estimates were largest over the Sierra Nevada, with seasonal differences exceeding 50 W m−2. Disagreements in Qsi were especially pronounced when averaging over high‐elevation basins, with monthly differences up to 80 W m−2. Biases in estimated Qsi predominantly occurred with darker than normal conditions associated with precipitation (a proxy for cloud cover), while the presence of aerosols and water vapor was unable to explain the biases. Users of Qsi estimates in regions of complex topography, especially those estimating Qsi to force land surface models, need to be aware of this source of uncertainty.

Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain

“…While overestimation of incident shortwave radiation has been a long-standing issue in many climate models and reanalyses, incident longwave radiation is typically underestimated (Katragkou et al, 2015;Li et al, 2016;Zib et al, 2012;Wild et al, 2017). The causes of the underestimation are, however, debated.…”

“…The causes of the underestimation are, however, debated. Li et al (2016) points to an improper representation of interactions between radiation and suspended frozen water particles in the atmosphere (solid hydrometeors) as a culprit, while Zib et al (2012) speculate that errors in simulated aerosols, water vapor content, and cloud properties (rather than cloud amounts) are the cause. Local issues such as longwave emissions from nearby terrain may also contribute to the deviations (Rontu et al, 2016).…”

Improvement of Hourly Surface Solar Irradiance Estimation Using MSG Rapid Scanning Service

“…Quantitative knowledge of Surface Solar Irradiance (SSI) is required in a wide range of scientific and technological applications. Complete and accurate SSI data at a specific site are highly important for regional crop growth modeling, evapotranspiration estimates and hydrological monitoring [1][2][3][4][5][6][7][8]. Furthermore, SSI is of fundamental importance to Earth's biosphere and climate [9]; SSI estimation also plays a significant role in the design of photovoltaic power and solar heating systems within the renewable energy industry [10][11][12].…”

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

“…By using both GLDAS and CERES, we overcome biases from failures to detect clouds in the 30 shortwave and longwave GLDAS and NLDAS (Cosgrove et al, 2003) products Hinkelman et al, 2015;Lapo et al, 2017). Specifically, CERES SYN provided incoming shortwave and longwave fluxes while GLDAS provided air temperature, wind speed, and specific humidity.…”