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Since its inception in 1983, NOAA's Regional Climate Center (RCC) Program has been providing timely, customized climate services for decision making across all climate-sensitive sectors. Through this 40-yr period, the RCC Program has not only seen but also has played an active role in, the evolution of climate services from the days of climate data libraries--where books of data were consulted to fulfill simple data requests--to coproduced tools that can calculate sectoral-specific, on-the-fly climate analyses in a matter of seconds. With new technologies emerging, the RCC Program is poised to build on its reputation as a trusted climate service provider by incorporating advanced methods for climate service delivery to continue to meet the needs of the nation. This publication will provide a look back at the evolution of regional climate services over the past 40 years, along with a vision for the future. KEYWORDS: Climate services; Decision support; History, 1. Celebrating 40 years of the Regional Climate Center Program The Regional Climate Center (RCC) Program has its roots in the National Climate Program Act of 1978, which recognized the [...]

Weather and climate extremes profoundly affect society and the environment, resulting in the loss of life, property, and habitat. The extremes discussed herein are causally related: extratropical storms account for the majority of extreme winds during the cold season, and extreme waves are largely driven by extreme winds. For assessment purposes, extremes are defined based on meteorological principles rather than physical destructiveness. Nevertheless, each of these extremes can result in substantial societal impacts. Estimates of extratropical storm activity primarily derive from two sources: atmospheric reanalyses and pressure-based indices. Reanalysis products have the advantage of uniform space and time fields on which to locate pressure minima or vorticity maxima, facilitating the identification of storm tracks. In contrast, pressure-based indices have the advantage of being directly computed from in situ observations, which often extend further back in time than most reanalyses.

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

The transition from manual to automated weather observations at US National Weather Service Offices has compromised the ability to use these data as a means for estimating global horizontal and direct solar radiation. The creation of long term model-derived solar radiation climatologies continues to rely on the in situ cloud data that these observations provide, since homogeneous and readily available satellite data does not span the transition. An existing semi-physical solar radiation model is revised to allow for the estimation of hourly solar radiation based on these observations. Model evaluation reveals that errors in solar radiation estimates are comparable to other contemporary solar radiation models that estimate global horizontal solar radiation on both daily (10-15% mean absolute error) and hourly (15 19% mean absolute error) timescales. Hourly mean absolute errors are similar for different sky conditions, while daily percent errors are similar between seasons. Model updates also allow for accurate estimates of solar radiation in various climate regimes; regional patterns in model bias are not evident. Keywords: Solar radiation; ASOS; Model