A new paradigm for medium-range severe weather forecasts: probabilistic random forest-based predictions

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4--8. RFs are trained with 9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with degrading skill thereafter. The RF-based forecasts exhibit tendencies to underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing objective skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range

Forecast dataset associated with “From Random Forests to Flood Forecasts: A Research to Operations Success Story”

Gridded forecasts from the Colorado State University-Machine Learning Probabilities (CSU-MLP) system for excessive rainfall prediction over the continental United States. The dataset includes probabilistic forecasts for days 1, 2, and 3 from the 2017, 2019, and 2020 versions of the CSU-MLP forecast system. For the day 2 and 3 forecasts, daily forecasts are included from 19 June 2018 through 15 October 2020; for day-1 forecasts a period from 15 March 2019 through 15 October 2020 is used.Because excessive rainfall is poorly defined and difficult to forecast, there is a need for tools for Weather Prediction Center (WPC) forecasters to use when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1--3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a ``first guess'' in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.This research and operational transition was supported by NOAA Joint Technology Transfer Initiative grants NA16OAR4590238 and NA18OAR4590378

Class I major histocompatibility complexes loaded by a periodate trigger

Class I major histocompatibility complexes (MHCs) present peptide ligands on the cell surface for recognition by appropriate cytotoxic T cells. The unstable nature of unliganded MHC necessitates the production of recombinant class I complexes through in vitro refolding reactions in the presence of an added excess of peptides. This strategy is not amenable to high-throughput production of vast collections of class I complexes. To address this issue, we recently designed photocaged MHC ligands that can be cleaved by a UV light trigger in the MHC bound state under conditions that do not affect the integrity of the MHC structure. The results obtained with photocaged MHC ligands demonstrate that conditional MHC ligands can form a generally applicable concept for the creation of defined peptide−MHCs. However, the use of UV exposure to mediate ligand exchange is unsuited for a number of applications, due to the lack of UV penetration through cell culture systems and due to the transfer of heat upon UV irradiation, which can induce evaporation. To overcome these limitations, here, we provide proof-of-concept for the generation of defined peptide−MHCs by chemical trigger-induced ligand exchange. The crystal structure of the MHC with the novel chemosensitive ligand showcases that the ligand occupies the expected binding site, in a conformation where the hydroxyl groups should be reactive to periodate. We proceed to validate this technology by producing peptide−MHCs that can be used for T cell detection. The methodology that we describe here should allow loading of MHCs with defined peptides in cell culture devices, thereby permitting antigen-specific T cell expansion and purification for cell therapy. In addition, this technology will be useful to develop miniaturized assay systems for performing high-throughput screens for natural and unnatural MHC ligands

Radiative decays of decuplet hyperons

We calculate the radiative decay widths of decuplet hyperons in a chiral constituent quark model including electromagnetic exchange currents between quarks. Exchange currents contribute significantly to the E2 transition amplitude, while they largely cancel for the M1 transition amplitude. Strangeness suppression of the radiative hyperon decays is found to be weakened by exchange currents. Differences and similarities between our results and other recent model predictions are discussed.Comment: 11 pages, 1 eps figure, revtex, accepted for publication in Phys. Rev.

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon-here, extreme precipitation-and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems-extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs-and several mechanisms-fronts, atmospheric rivers, and orographic ascent-have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.11Ysciescopu

The 2015 Plains Elevated Convection at Night Field Project

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings

Near-Surface Thermodynamic Sensitivities in Simulated Extreme-Rain-Producing Mesoscale Convective Systems

The article of record as published may be found at http://dx.doi.org/10.1175/MWR-D-16-0255.1This study investigates the influences of low-level atmospheric water vapor on the precipitation produced by simulated warm-season midlatitude mesoscale convective systems (MCSs). In a series of semi-idealized numerical model experiments using initial conditions gleaned from composite environments from observed cases, small increases in moisture were applied to the model initial conditions over a layer either 600 m or 1 km deep. The precipitation produced by the MCS increased with larger moisture perturbations as expected, but the rainfall changes were disproportionate to the magnitude of the moisture perturbations. The experiment with the largest perturbation had a water vapor mixing ratio increase of approximately 2 g kg2¯¹ over the lowest 1 km, corresponding to a 3.4% increase in vertically integrated water vapor, and the area-integrated MCS precipitation in this experiment increased by nearly 60% over the control. The locations of the heaviest rainfall also changed in response to differences in the strength and depth of the convectively generated cold pool. The MCSs in environments with larger initial moisture perturbations developed stronger cold pools, and the convection remained close to the outflow boundary, whereas the convective line was displaced farther behind the outflow boundary in the control and the simulations with smaller moisture perturbations. The high sensitivity of both the amount and location of MCS rainfall to small changes in low-level moisture demonstrates how small moisture errors in numerical weather prediction models may lead to large errors in their forecasts of MCS placement and behavior.National Science FoundationAGS-1359727AGS-PRF 152443