Julia Jeworrek’s research while affiliated with University of British Columbia and other places

Analog ensembles (AnEns) traditionally use a single numerical weather prediction (NWP) model to make a forecast, then search an archive to find a number of past similar forecasts (analogs) from that same model, and finally retrieve the actual observations corresponding to those past forecasts to serve as members of an ensemble forecast. This study investigates new statistical methods to combine analogs into ensemble forecasts and validates them for 3-hourly precipitation over the complex terrain of British Columbia, Canada. Applying the past analog error to the target forecast (instead of using the observations directly) reduces the AnEn dry bias and makes prediction of heavy-precipitation events probabilistically more reliable—typically the most impactful forecasts for society. Two variants of this new technique enable AnEn members to obtain values outside the distribution of the finite archived observational dataset—that is, they are theoretically capable of forecasting record events, whereas traditional analog methods cannot. While both variants similarly improve heavier precipitation events, one variant predicts measurable precipitation more often, which enhances accuracy during winter. A multimodel AnEn further improves predictive skill, albeit at higher computational cost. AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final AnEn prediction system improves the skill and reliability of point forecasts across all precipitation intensities. Significance Statement The analog ensemble (AnEn) technique is a data-driven method that can improve local weather forecasts. It improves raw model forecasts using past similar model predictions and observations, reducing future forecast errors and providing probabilities for a range of possible outcomes. One limitation of AnEns is that they commonly tend to make rare-event (e.g., heavy precipitation) forecasts appear less extreme. Usually, heavier precipitation events have a higher impact on society and the economy. This study introduces two new AnEn techniques that make operational forecasts of both probabilities and most likely amounts more accurate for heavy precipitation.

This study systematically explores existing and new optimization techniques for analog ensemble (AnEn) post-processing of hourly to daily precipitation forecasts over the complex terrain of southwest British Columbia, Canada. An AnEn bias-corrects a target model forecast by searching for past dates with similar model forecasts (i.e., analogs), and using the verifying observations as ensemble members. The weather variables (i.e., predictors) that select the best past analogs vary among stations and seasons. First, different predictor selection techniques are evaluated and we propose an adjustment in the forward selection procedure that considerably improves computational efficiency while preserving optimization skill. Second, temporal trends of predictors are used to further enhance predictive skill, especially at shorter accumulation windows and longer forecast horizons. Finally, this study introduces a modification in the analog search that allows for selection of analogs within a time window surrounding the target lead time. These supplemental lead times effectively expand the training sample size, which significantly improves all performance metrics—even more than the predictor weighting and temporal-trend optimization steps combined. This study optimizes AnEns for moderate precipitation intensities but also shows good performance for the ensemble median and heavier precipitation rates. Precipitation is most challenging to predict at finer temporal resolutions and longer lead times, yet those forecasts see the largest enhancement in predictive skill from AnEn post-processing. This study shows that optimization of AnEn post-processing, including new techniques developed herein, can significantly improve computational efficiency and forecast performance.

Physics parameterizations in the Weather Research and Forecasting (WRF) model are systematically varied to investigate precipitation forecast performance over the complex terrain of southwest British Columbia (BC). Comparing a full year of modelling data from over 100 WRF configurations to station observations reveals sensitivities of precipitation intensity, season, location, grid resolution, and accumulation window. The choice of cumulus and microphysics parameterizations is most important. The WSM5 microphysics scheme yields competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although the cale-aware Grell-Freitas cumulus parameterization performs better for summertime convective precipitation, the conventional Kain-Fritsch parameterization better simulates wintertime frontal precipitation, which contributes to the majority of the annual precipitation in southwest BC. Finer grid spacings have lower relative biases and a more realistic spread in precipitation intensity distribution, yet higher relative standard deviations of their errors — they produce finer spatial differences and local extrema. Finer resolutions produce the best fraction of correct-to-incorrect forecasts across all precipitation intensities, whereas the coarser 27-km domain yields the highest hit rates and equitable threat scores. Verification metrics improve greatly with longer accumulation windows — hourly precipitation values are prone to double-penalty issues, while longer accumulation windows compensate for timing errors but lose information about short-term precipitation intensities. This study provides insights regarding WRF precipitation performance in complex terrain across a wide variety of configurations, using metrics important to a range of end users.

The formation of convective sea-effect snowfall (i.e., snow bands) is triggered by cold air outbreaks over a relatively warm and open sea. Snow bands can produce intense snowfall which can last for several days over the sea and potentially move towards the coast depending on wind direction. We defined the meteorological conditions which statistically favor the formation of snow bands over the north-eastern Baltic Sea of the Finnish coastline and investigated the spatio-temporal characteristics of these snow bands. A set of criteria, which have been previously shown to be able to detect the days favoring sea-effect snowfall for Swedish coastal area, were refined for Finland based on four case study simulations, utilizing a convection-permitting numerical weather prediction (NWP) model (HARMONIE-AROME). The main modification of the detection criteria concerned the threshold for 10 m wind speed: the generally assumed threshold value of 10 m s−1 was decreased to 7 m s−1. The refined criteria were then applied to regional climate model (RCA4) data, for an 11-year time period (2000–2010). When only considering cases in Finland with onshore wind direction, we found on average 3 d yr−1 with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline. Statistics of the favorable days indicated that the lower 10 m wind speed threshold improved the representation of the frequency of snow bands. For most of the favorable snow band days, the location and order of magnitude of precipitation were closely captured, when compared to gridded observational data for land areas and weather radar reflectivity images. Lightning were observed during one third of the favorable days over the Baltic Sea area.

This study evaluates the grid-length dependency of the Weather Research and Forecasting (WRF) Model precipitation performance for two cases in the Southern Great Plains of the United States. The aim is to investigate the ability of different cumulus and microphysics parameterization schemes to represent precipitation processes throughout the transition between parameterized and resolved convective scales (e.g., the gray zone). The cases include the following: 1) a mesoscale convective system causing intense local precipitation, and 2) a frontal passage with light but continuous rainfall. The choice of cumulus parameterization appears to be a crucial differentiator in convective development and resulting precipitation patterns in the WRF simulations. Different microphysics schemes produce very similar outcomes, yet some of the more sophisticated schemes have substantially longer run times. This suggests that this additional computational expense does not necessarily provide meaningful forecast improvements, and those looking to run such schemes should perform their own evaluation to determine if this expense is warranted for their application. The best performing cumulus scheme overall for the two cases studies here was the scale-aware Grell–Freitas cumulus scheme. It was able to reproduce a smooth transition from subgrid- (cumulus) to resolved-scale (microphysics) precipitation with increasing resolution. It also produced the smallest errors for the convective event, outperforming the other cumulus schemes in predicting the timing and intensity of the precipitation.

Convective snow bands develop in response to a cold air outbreak from the continent or the frozen sea over the open water surface of lakes or seas. The comparatively warm water body triggers shallow convection due to increased heat and moisture fluxes. Strong winds can align with this convection into wind-parallel cloud bands, which appear stationary as the wind direction remains consistent for the time period of the snow band event, delivering enduring snow precipitation at the approaching coast. The statistical analysis of a dataset from an 11-year high-resolution atmospheric regional climate model (RCA4) indicated 4 to 7 days a year of moderate to highly favourable conditions for the development of convective snow bands in the Baltic Sea region. The heaviest and most frequent lake effect snow was affecting the regions of Gävle and Västervik (along the Swedish east coast) as well as Gdansk (along the Polish coast). However, the hourly precipitation rate is often higher in Gävle than in the Västervik region. Two case studies comparing five different RCA4 model setups have shown that the Rossby Centre atmospheric regional climate model RCA4 provides a superior representation of the sea surface with more accurate sea surface temperature (SST) values when coupled to the ice–ocean model NEMO as opposed to the forcing by the ERA-40 reanalysis data. The refinement of the resolution of the atmospheric model component leads, especially in the horizontal direction, to significant improvement in the representation of the mesoscale circulation process as well as the local precipitation rate and area by the model.