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Yang Zhou
,
Binshuo Liu
,
Boyang Lei
,
Qifan Zhao
,
Shanlei Sun
, and
Haishan Chen

Abstract

The ERA5 reanalysis during cold months (November-March) of 1979-2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. EOF was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolutions of ridge and trough over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of vorticity tendency at 500 hPa. For the reforecasts during 2004-2019 in the CMA S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of the jet activities and the H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.

Restricted access
Chih-Chi Hu
,
Peter Jan van Leeuwen
, and
Jeffrey L. Anderson

Abstract

The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an Observing System Simulation Experiment (OSSE) in a simplified atmospheric general circulation model, and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a year-long cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.

Restricted access
Xinxin Xie
,
Xiao Xiao
,
Jieying He
,
Pablo Saavedra Garfias
,
Tiejian Li
,
Xiaoyu Yu
,
Songyan Gu
, and
Yang Guo

Abstract

This study investigates precipitation observed by a set of collocated ground-based instruments in Zhuhai, a coastal city located at the southern tip of the Pearl River Delta of Guangdong Province in South China. Seven months of ground-based observations from a tipping-bucket rain gauge (RG), two laser disdrometers (PARSIVEL and Present Weather Sensor 100 (PWS)], and a vertically pointing Doppler Micro Rain Radar-2 (MRR), spanning from December 2021 to July 2022, are statistically evaluated to provide a reliable reference for China’s spaceborne precipitation measurement mission. Rainfall measurement discrepancies are found between the instruments though the collocated deployment mitigates uncertainties originating from spatial/temporal variabilities of precipitation. The RG underestimates hourly rain amounts at the observation site, opposite to previous studies, leading to a percent bias (Pbias) of 18.2% of hourly rain amounts when compared to the PARSIVEL. With the same measurement principle, the hourly accumulated rain between the two laser disdrometers has a Pbias of 15.3%. Discrepancies between MRR and disdrometers are assumed to be due to different temporal/spatial resolution, instrument sensitivities, and observation geometry, with a Pbias of mass-weighted mean diameter and normalized intercept parameter of gamma size distribution less than 9%. The vertical profiles of drop size distribution (DSD) derived from the MRR are further examined during extreme rainfalls in the East Asian monsoon season (May, June, and July). Attributed to the abundant moisture which favors the growth of raindrops, coalescence is identified as the predominant effective process, and the raindrop mass-weighted mean diameter increases by 33.7% when falling from 2000 to 600 m during the extreme precipitation event in May.

Significance Statement

The performance and reliability of ground-based observations during precipitation scenarios are evaluated over the coastal area of South China, in preparation for China’s spaceborne precipitation measurement mission. A comparison study, which is carried out to assess the accuracy of rainfall and drop size distribution (DSD), demonstrates that the observation results are relatively reliable though discrepancies between the instruments still exist, while the accompanying microphysical process during extreme precipitation can be quantified with profiling capabilities at the observatory. An accurate and reliable rainfall characterization over the coastal region in South China can contribute to the validation of satellite rainfall products and provide further insights into the microphysical parameterization schemes during extreme precipitation.

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Xuan Zhou
,
Lu Wang
,
Pang-chi Hsu
,
Tim Li
, and
Baoqiang Xiang

Abstract

The prediction skill for individual Madden-Julian Oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the Subseasonal-to-Seasonal (S2S) Phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection-circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high- (low-) skill events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect through weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS.

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William Rudisill
,
Alan Rhoades
,
Zexuan Xu
, and
Daniel R. Feldman

Abstract

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

Open access
Sharanya J. Majumdar
,
David Hoffmann
,
Elizabeth E. Ebert
, and
Brian W. Golding

Abstract

University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on Value Chain Approaches to Evaluate the End-to-End Warning Chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.

Two institutions took contrasting approaches to using the questionnaire. At the University of Miami, teams of meteorology undergraduates evaluated the value chain for three hurricanes. Among the issues identified were the dynamic nature of the forecasts, misinterpretations of the products, social media influences, demographic factors, and disparities in responses. The Australian Bureau of Meteorology engaged student interns in different disciplines and experience levels to evaluate and contrast the warning value chains for domestic and international events.

The students expressed enthusiasm for the exercises. Educational benefits included team collaboration, critical thinking, research and composition skills, a comprehensive view of weather events, understanding information flow, learning about new tools, and identifying gaps in practices. We encourage educators to adopt similar exercises to enable students to develop these skills, adopt value chain ideas, and contribute meaningfully to the community. The level of maintenance is low, and there is flexibility in how the exercises can be developed.

Open access
Louise Crochemore
,
Stefano Materia
,
Elisa Delpiazzo
,
Stefano Bagli
,
Andrea Borrelli
,
Francesco Bosello
,
Eva Contreras
,
Francesco Dalla Valle
,
Silvio Gualdi
,
Javier Herrero
,
Francesca Larosa
,
Rafael Lopez
,
Valerio Luzzi
,
Paolo Mazzoli
,
Andrea Montani
,
Isabel Moreno
,
Valentina Pavan
,
Ilias Pechlivanidis
,
Fausto Tomei
,
Giulia Villani
,
Christiana Photiadou
,
María José Polo
, and
Jaroslav Mysiak

Abstract

Assessing the information provided by coproduced climate services is a timely challenge, given the continuously evolving scientific knowledge and its increasing translation to address societal needs. Here, we propose a joint evaluation and verification framework to assess prototype services that provide seasonal forecast information based on the experience from the Horizon 2020 (H2020) Climate forecasts enabled knowledge services (CLARA) project. The quality and value of the forecasts generated by CLARA services were first assessed for five climate services utilizing the Copernicus Climate Change Service seasonal forecasts and responding to knowledge needs from the water resources management, agriculture, and energy production sectors. This joint forecast verification and service evaluation highlights various skills and values across physical variables, services, and sectors, as well as a need to bridge the gap between verification and user-oriented evaluation. We provide lessons learned based on the service developers’ and users’ experience and recommendations to consortia that may want to deploy such verification and evaluation exercises. Last, we formalize a framework for joint verification and evaluation in service development, following a transdisciplinary (from data purveyors to service users) and interdisciplinary chain (climate, hydrology, economics, and decision analysis).

Open access
Feimin Zhang
,
Shang Wan
,
Shuanglong Jin
, and
Hao Wang

Abstract

Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbine within wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that, compared to the simulations without assimilation, the predicted ultra-short term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly, while by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly, and could obviously reduce prediction error.

Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.

Restricted access
Garik Gutman
,
Roger Pielke Sr
,
Richard Anthes
,
Pinhas Alpert
,
Alexander Baklanov
,
Svante Bodin
,
Alexander Khain
, and
Simon Krichak

Abstract

On March 5, 2023, Professor Lev Gutman would have been 100 years old. This article describes Professor Gutman’s legacy in the field of dynamic mesoscale meteorology and numerical weather prediction. Gutman developed his career as a mathematician and meteorologist in the Soviet Union, where he built a school of specialists in mesoscale meteorology during the 1950s through the 1970s. He primarily worked on analytical methods to solve complex nonlinear problems, such as the structure of sea breezes, mountain-valley circulations, and thermal convection over heated terrain. Gutman pioneered the development of theories of cumulus clouds, tornados, and other atmospheric phenomena. In the 1960s, he carried out numerous research investigations on these topics with his doctoral students and collaborators at High-Altitude Geophysical Institute in Nalchik in the northern Caucasus and later at the Siberian scientific center near Novosibirsk. Gutman compiled the results from these studies into a monograph titled “Introduction to the Nonlinear Theory of Mesoscale Meteorological Processes”, which was published in Russian in 1969, and later translated into English, Chinese, and Japanese. This monograph became a major textbook for specialists in mesoscale meteorology, remaining relevant to this day. After Prof. Gutman immigrated to Israel in 1978, his collaborations expanded to include Israeli and western scientists from Europe and the United States. Gutman did not receive the recognition he deserved due to the political realities of the time. His book and his seminal analytical solutions should still be useful for early career scientists in mesoscale meteorology and atmospheric dynamics.

Open access
Hans Burchard
,
Matthew Alford
,
Manita Chouksey
,
Giovanni Dematteis
,
Carsten Eden
,
Isabelle Giddy
,
Knut Klingbeil
,
Arnaud Le Boyer
,
Dirk Olbers
,
Julie Pietrzak
,
Friederike Pollmann
,
Kurt Polzin
,
Fabien Roquet
,
Pablo Sebastia Saez
,
Sebastiaan Swart
,
Lars Umlauf
,
Gunnar Voet
, and
Bethan Wynne-Cattanach
Open access