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We describe a method for quantifying the contribution of climate change to local monthly, seasonal, and annual mean temperatures for locations where long observational temperature records are available. The method is based on estimating the change in the monthly mean temperature distribution due to climate change using CMIP6 (Coupled Model Intercomparison Project Phase 6) model data. As a case study, we apply the method to the record‐warm September 2023 in Helsinki, and then briefly examine all record‐warm months of the 21st century. Our results suggest that climate change made the record‐warm September in Helsinki 9.4 times more likely and 1.4°C warmer. Thus, the new monthly mean record in September 2023 would probably not have been set without the observed global warming. The presented and provided tool allows operational meteorologists and climatologists to monitor and report the impact of climate change on local temperatures in near real time. We describe a method for quantifying the contribution of climate change to local monthly mean temperatures. As a case study, we apply the method to the record‐warm September of 2023 in Helsinki. We show that climate change made the record‐warm September about nine times more likely and 1.4°C warmer than it would have been without human‐induced climate change.

This chapter contains sections titled: A brief history of operational weather forecasting

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space-time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

In recent years, the availability of crowd‐sourced weather measurements has increased substantially. Yet, despite offering an insight into the weather where people live, these measurements are not currently being utilized by public weather services in the operational objective verification of forecasts. Here, we explore the use of crowd‐sourced temperature observations from the Weather Observations Website (WOW) to verify and compare the performance of the Met Office's replacement post‐processing system, known as IMPROVER, against the old system. It is found that, even after quality control, the WOW data still has up to five times the number of sites compared to the official surface network. The overall errors are marginally worse than using the official network; for example, the Mean Absolute Error is approximately 0.2 K larger for IMPROVER verified with WOW over SYNOP sites. However, 95% of the errors at all quality‐controlled WOW sites are less than or equal to 2.5 K, and 70% of the errors are less than or equal to 1 K, indicating a good level of consistency with the forecasts. The sensitivity of the results to quality control depends on the choice of error metric. Finally, given the degree of consistency, quantity, and location of good‐quality WOW data, it is recommended that crowd‐sourced data continue to be used as an operational verification truth source in conjunction with the official surface network. This research objectively evaluates crowd‐sourced temperature observations from the Weather Observations Website (WOW) for use in operational verification. It is found that a significant proportion of sites are viable, with little sensitivity to quality control.

NowCastMIX is the core nowcasting guidance system at the German Weather Service. It automatically monitors several systems to capture rapidly developing high-impact mesoscale convective events, including 3D radar volume scanning, radar-based cell tracking and extrapolation, lightning detection, calibrated precipitation extrapolations, NWP, and live surface station reports. Within the context of the larger warning decision support process AutoWARN, NowCastMIX integrates the input data into a high-resolution analysis, based on a fuzzy logic approach for thunderstorm categorization and extrapolation, to provide an optimized warning solution with a 5-min update cycle for lead times of up to 1 h. Feature tracking is undertaken to optimize the direction of warning polygons, allowing individual, tangentially moving cells or cell clusters to be tracked explicitly. An adaptive ensemble clustering is deployed to reduce the spatial complexity of the resulting warning fields and smooth noisy temporal variations to a manageable level for duty forecasters. Further specialized outputs for civil aviation and for a public mobile phone warning app are generated. Now in its eighth year of operation, a comprehensive and complete set of thunderstorm analyses and nowcasts over Germany has been created, which is of unique value for ongoing research and development efforts for improving the system, as well as for addressing climatological aspects of severe convection. Verification has shown that NowCastMIX has helped to significantly improve the quality of the official warnings for severe convective weather events when used within the AutoWARN process.

GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called ‘‘coherence.’’ This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for post- processing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative post- processing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach

This study explores forecaster perceptions of emerging needs for probabilistic forecasting of winter weather hazards through a nationwide survey disseminated to National Weather Service (NWS) forecasters. Questions addressed four relevant thematic areas: 1) messaging timelines for specific hazards, 2) modeling needs, 3) current preparedness to interpret and communicate probabilistic winter information, and 4) winter forecasting tools. The results suggest that winter hazards are messaged on varying time scales that sometimes do not match the needs of stakeholders. Most participants responded favorably to the idea of incorporating new hazard-specific regional ensemble guidance to fill gaps in the winter forecasting process. Forecasters provided recommendations for ensemble run length and output frequencies that would be needed to capture individual winter hazards. Qualitatively, forecasters expressed more difficulties communicating, rather than interpreting, probabilistic winter hazard information. Differences in training and the need for social-science-driven practices were identified as a few of the drivers limiting forecasters’ ability to provide strategic winter messaging. In the future, forecasters are looking for new winter tools to address forecasting difficulties, enhance stakeholder partnerships, and also be useful to the local community. On the regional scale, an ensemble system could potentially accommodate these needs and provide specialized guidance on timing and sensitive/high-impact winter events.

Using vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 0°C consist of positive energy; those colder than 0°C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters.

The quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.