Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

Testing Vertical Wind Shear and Nonlinear MJO/ENSO Interactions as Predictors for Subseasonal Atlantic Tropical Cyclone Forecasts

Hansen et al. (2020) found patterns of vertical wind shear, relative humidity (RH) and non-linear interactions between the Madden-Julian Oscillation and El Niño-Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical-dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions, however, shear predictors improve forecast skill at 5-10 day lead times, before forecast shear errors become too large. Non-linear MJO/ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both non-linear MJO/ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave-breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.

An Analysis of the 3 May 2020 Low-Predictability Derecho Using a Convection-Allowing MPAS Ensemble

This study analyzes the low short-range predictability of the 3 May 2020 derecho using a 40-member convection-allowing Model for Prediction Across Scales (MPAS) ensemble. Elevated storms formed in south-central Kansas late at night and evolved into a progressive mesoscale convective system (MCS) during the morning while moving across southern Missouri and northern Arkansas, and affected western and middle Tennessee and southern Kentucky in the afternoon. The convective initiation (CI) in south-central Kansas, the organization of a dominant bow echo MCS and the MCS maintenance over Tennessee were identified as the three main predictability issues. These issues were explored using three MPAS ensemble members, observations and the Rapid Refresh analyses. The MPAS members were classified as successful or unsuccessful with regard to each predictability issue. CI in south-central Kansas was sensitive to the temperature and dewpoint profiles in low levels, which were associated with greater elevated thermodynamic instability and lower level of free convection in the successful member. The subsequent organization of a dominant bowing MCS was well predicted by the member that had more widespread convection in the early stages and no detrimental interaction with other simulated convective systems. Lastly, the inability of MPAS ensemble members to predict the MCS maintenance over western and middle Tennessee was linked to a dry bias in low levels and much lower thermodynamic instability ahead of the MCS compared to observations. This case demonstrates the challenges in operational forecasting of warm-season derecho-producing progressive MCSs, particularly when ensemble numerical weather prediction guidance solutions differ considerably.

Exploring the differences in SPC convective outlook interpretation using categorical and numeric information

While previous work has shown the Storm Prediction Center (SPC) convective outlooks accurately capture meteorological outcomes, evidence suggests stakeholders and the public may misinterpret the categorical words currently used in the product. This work attempts to address this problem by investigating public reactions to alternative information formats that include numeric information: (1) numeric risk levels (i.e., “Level 2 of 5”) and (2) numeric probabilities (i.e., “a 5% chance”). In addition, it explores how different combinations of the categorical labels with numeric information may impact public reactions to the product. Survey data comes from the 2020 Severe Weather and Society Survey, a nationally representative survey of US adults. Participants were shown varying combinations of the information formats of interest, and then rated their concern about the weather and the likelihood of changing plans in response to the given information. Results indicate that providing numeric information (in the form of levels or probabilities) increases the likelihood of participants correctly interpreting the convective outlook information relative to categorical labels alone. Including the categorical labels increases misinterpretation, regardless of whether numeric information was included alongside the labels. Finally, findings indicate participants’ numeracy (or their ability to understand and work with numbers) had an impact on correct interpretation of the order of the outlook labels. Although there are many challenges to correctly interpreting the SPC convective outlook, using only numeric labels instead of the current categorical labels may be a relatively straightforward change that could improve public interpretation of the product.

High-resolution NWP forecast precipitation comparison over complex terrain of the Sierras de Córdoba during RELAMPAGO-CACTI.

Sierras de Córdoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This motivates the forecast performance evaluation of 24-hour accumulated precipitation and vertical profiles of atmospheric variables from different numerical weather prediction (NWP) models with the final aim of helping water management in the region. The NWP models evaluated include the Global Forecast System (GFS) which parameterizes convection, and convection-permitting simulations of the Weather Research and Forecasting Model (WRF) configured by three institutions: University of Illinois at Urbana–Champaign (UIUC), Colorado State University (CSU) and National Meteorological Service of Argentina (SMN). These models were verified with daily accumulated precipitation data from rain gauges and soundings during the RELAMPAGO-CACTI field campaign. Generally all configurations of the higher-resolution WRFs outperformed the lower-resolution GFS based on multiple metrics. Among the convection-permitting WRF models, results varied with respect to rainfall threshold and forecast lead time, but the WRFUIUC mostly performed the best. However, elevation dependent biases existed among the models that may impact the use of the data for different applications. There is a dry (moist) bias in lower (upper) pressure levels which is most pronounced in the GFS. For Córdoba an overestimation of the northern flow forecasted by the NWP configurations at lower levels was encountered. These results show the importance of convection-permitting forecasts in this region, which should be complementary to the coarser-resolution global model forecasts to help various users and decision makers.

Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

The National Severe Storms Lab (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly-updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 km to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using eleven case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved mid-level mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic mid-level rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0-3 km storm-relative helicity.

The ability of the ICE-T microphysics scheme in HARMONIE-AROME to predict aircraft icing

In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilised by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat-CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighbourhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighbourhood areas. With a higher frequency of forecasted icing, a higher false-alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

Communicating Properties of Changes in Lagged Weather Forecasts

Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.

The Performance of ECMWF sub-seasonal forecasts to predict the Rainy Season Onset Dates in Vietnam

The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Sub-seasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic sub-regions of Vietnam. The results show that the ECMWF model reproduces well the observed inter-annual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all sub-regions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all sub-regions indicate that the model outperforms the observed climatology in predicting the RSOD at sub-seasonal lead times (~28–35 days). However, the model is overall more skilful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analysing the highest skill score for each sub-region at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.

Performance of 2020 Real-Time Atlantic Hurricane Forecasts from High-Resolution Global-Nested Hurricane Models: HAFS-globalnest and GFDL T-SHiELD

The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth- to- Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skill as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4-5 day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.