The role of physical scheme interactions on warm season rainfall forecasts

Despite numerous efforts that have been undertaken to improve rainfall forecasts it still remains the most poorly forecasted meteorological variable. Errors in simulated rainfall arise as a result of errors in both initial conditions and numerical models. To compensate for these limitations, in recent years ensemble forecasting has been increasingly used. At first, ensembles were designed based on perturbed initial conditions, while recently use of mixed-physics and mixed-model ensembles for rainfall forecasting have been extensively investigated;The main objective of the present study was to help optimizing a mixed physics ensemble for warm season MCS rainfall forecasting by evaluating the impact that various physical schemes as well as their interactions have on rainfall forecasts. In addition, the work investigated how the impact of the physical schemes and their interaction changed when different initial conditions were used. For this purpose, high resolution (12-km grid spacing, 34 vertical levels) simulations from the Weather Research and Forecasting (WRF) model of 8 International H2O Project events were examined. For each event a matrix of 18 WRF model configurations was created by varying the convective parameterization scheme, the PBL scheme, and microphysical schemes. In order to quantify the impact of varying two different model physical schemes on the simulated rainfall field, the factor separation methodology was used

Variability in warm-season mesoscale convective system rainfall predictability

Knowledge that certain large-scale environments might be better simulated than others, or might favor a specific model configuration, can be very valuable for operational forecasting. Present study involves a detail investigation of variations in skill of MCS rainfall forecast among events characterized with different magnitudes of large-scale forcing, along with variations in forecast skill among events due to the use of different convective parameterizations (Betts-Miller-Janjic and Kain-Fritsch). For this purpose simulations of twenty warm season MCS events over the Upper Midwest performed using a workstation version of the National Centers for Environmental Prediction (NCEP) Eta model with ten kilometer grid spacing are used. In addition, an impact of three different types of adjustments to initial conditions (cold pool initialization, vertical assimilation of mesoscale surface observations and relative humidity adjustment based on radar echo coverage) on rainfall forecast skill is investigated

The 4 June 1999 Derecho event: A particularly difficult challenge for numerical weather prediction

Warm season convective system rainfall forecasts remain a particularly difficult forecast challenge. For these events, it is possible that ensemble forecasts would provide helpful information unavailable in a single deterministic forecast. In this study, an intense derecho event accompanied by a well-organized band of heavy rainfall is used to show that for some situations, the predictability of rainfall even within a 12-24-h period is so low that a wide range of simulations using different models, different physical parameterizations, and different initial conditions all fail to provide even a small signal that the event will occur. The failure of a wide range of models and parameterizations to depict the event might suggest inadequate representation of the initial conditions. However, a range of different initial conditions also failed to lead to a well-simulated event, suggesting that some events are unlikely to be predictable with the current observational network, and ensemble guidance for such cases may provide limited additional information useful to a forecaster

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in nearrealtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9km horizontal grid spacing. The ensemble has eight members, all employing WRFARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 20122013 and 20132014 winters. A smaller domain covering just the West Coast was created to minimize bandwidth consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Shortterm Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6hr QPF along with the ExREF mean 6hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented

The LSST AGN Data Challenge: Selection methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidatesComment: Accepted by ApJ. 21 pages, 14 figures, 5 table

The LSST Era of Supermassive Black Hole Accretion Disk Reverberation Mapping

peer reviewedThe Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will detect an unprecedentedly large sample of actively accreting supermassive black holes with typical accretion disk (AD) sizes of a few light days. This brings us to face challenges in the reverberation mapping (RM) measurement of AD sizes in active galactic nuclei using interband continuum delays. We examine the effect of LSST cadence strategies on AD RM using our metric AGN_TimeLagMetric. It accounts for redshift, cadence, the magnitude limit, and magnitude corrections for dust extinction. Running our metric on different LSST cadence strategies, we produce an atlas of the performance estimations for LSST photometric RM measurements. We provide an upper limit on the estimated number of quasars for which the AD time lag can be computed within 0 1000 sources in each deep drilling field (DDF; (10 deg2)) in any filter, with the redshift distribution of these sources peaking at z ≍ 1. We find the LSST observation strategies with a good cadence (≲5 days) and a long cumulative season (~9 yr), as proposed for LSST DDF, are favored for the AD size measurement. We create synthetic LSST light curves for the most suitable DDF cadences and determine RM time lags to demonstrate the impact of the best cadences based on the proposed metric