Application of temporal streamflow descriptors in hydrologic model parameter estimation

This paper presents a parameter estimation approach based on hydrograph descriptors that capture dominant streamflow characteristics at three timescales (monthly, yearly, and record extent). The scheme, entitled hydrograph descriptors multitemporal sensitivity analyses (HYDMUS), yields an ensemble of model simulations generated from a reduced parameter space, based on a set of streamflow descriptors that emphasize the timescale dynamics of streamflow record. In this procedure the posterior distributions of model parameters derived at coarser timescales are used to sample model parameters for the next finer timescale. The procedure was used to estimate the parameters of the Sacramento soil moisture accounting model (SAC-SMA) for the Leaf River, Mississippi. The results indicated that in addition to a significant reduction in the range of parameter uncertainty, HYDMUS improved parameter identifiability for all 13 of the model parameters. The performance of the procedure was compared to four previous calibration studies on the same watershed. Although our application of HYDMUS did not explicitly consider the error at each simulation time step during the calibration process, the model performance was, in some important respects, found to be better than in previous deterministic studies. Copyright 2005 by the American Geophysical Union

Sources of predictability for deep convection

The advent of convection-permitting ensemble prediction systems at most operational weather centers within the last 15 years constitutes a step-change in our ability to forecast convection. This is a fundamental task for weather services, as not only the general public demands good and reliable forecasts of convection, but they can also be accompanied by heavy precipitation and destructive hail, and so comprise a risk to human life. Unfortunately, their prediction is challenging and their predictability limited by the chaotic nature of the atmosphere, so there is an intrinsic limit to their prediction in the order of a few hours. But for all that, the possible existence of sources of predictability that are able to extend the forecast horizon on the convective scales is being discussed. More precisely, those scales might inherit predictability from larger-scale features, such as orography or the prevailing weather regime. Furthermore, radar data will be operationally assimilated at \acf{DWD} within the year and provide similar information as the natural sources of predictability---the position of convection. The identification of predictability limits of convection poses significant challenges, which we address within a hierarchy of model configurations, combined with radar DA and two sets of \acfp{IC}. First, we reduce the complexity in an idealized setup with simplified \acp{IC} and orography before we reintroduce complex orography and natural variability of the synoptic weather regime. We apply a combination of sophisticated verification metrics to address specific facets of practical predictability, namely the predictability of the model state and the model predictability of the atmospheric state. In the idealized setup, we find increased predictability of convection in the presence of orography if the \acp{IC} depict only small-scale errors, representing perfect large-scale predictability. In the presence of large-scale errors, the beneficial effect of the orography is greatly diminished. However, the assimilation of radar observations proved its ability to account for these errors and provide high-quality analyses. In both sets of \acp{IC}, the forecast horizon is extended by \SI{6}{h}, where scales up to \SI{50}{km} remain predictable for small-scale errors, and scales up to \SI{100}{km} do so with additional large-scale uncertainty. The experiments also highlight increased predictability for convection with a high level of organization. We were also able to confirm these results in the pre-operational COSMO-KENDA system of the \ac{DWD}. More specifically, we found increased predictability of the model state and model predictability of the atmospheric state in the orographically more influenced South than in the comparatively plain North of Germany. Throughout three summers in an older \ac{DWD} system, we found locally forced weather situations to be less predictable than those forced by the synoptic weather regime and no significant effect of orography.Die Einführung von Konvektion auflösenden Vorhersagemodellen in den meisten operationellen Wetterzentren während der letzten 15 Jahre markiert einen Fortschritt in der Vorhersagbarkeit von Konvektion. Ein öffentliches Interesse an akkuraten Gewittervorhersagen ebenso wie die mit Gewittern verbundenen Sicherheitsrisiken machen diese zu einer zentrale Aufgabe für Wetterdienste. Eine Herausforderung ergibt sich hierbei aus der natürlichen Begrenztheit der Vorhersage auf wenige Stunden aufgrund der chaotischen Eigenschaften der Atmosphäre. Jedoch gibt es Hypothesen zu möglichen Quellen von Vorhersagbarkeit, die den Vorhersagehorizont auf den konvektiven Skalen erweitern können. Diese Skalen könnten Vorhersagbarkeit von anderen Merkmalen, wie z.B. Orographie oder dem Wetterregime, übernehmen. Darüberhinaus plant der Deutsche Wetterdienst noch 2019 operationell Radardaten zu assimilieren, was ebenso wie Orographie die Position von Konvektion beeinflusst. Wir nähern uns der Ermittlung der Grenzen von Gewittervorhersagbarkeit mittels einer Reihe von Modellkonfigurationen, kombiniert mit Radardatenassimilation und zweierlei Arten von Anfangsbedingungen, an. Wir verringern die Komplexität in einem idealisierten Setup mit vereinfachten Anfangsbedingungen (AB) und Orographie, ehe wir komplexe Orographie und die natürlich Variabilität des synoptischen Wetterregimes erneut einführen. Wir wenden eine Kombination von Verifikationsmethoden an und berücksichtigen so spezifische Facetten der praktischen Vorhersagbarkeit, d.h. der Modellvorhersagbarkeit und derjenigen der Atmosphäre. Im idealisierten Setup finden wir gesteigerte Vorhersagbarkeit von Konvektion unter Einfluss von Orographie, sofern die AB nur kleinskalige Fehler aufweisen, die eine perfekte synoptische Vorhersage repräsentieren. Wenn synoptische Fehler auftreten, wird die positive Wirkung der Orographie verringert. Jedoch kann die Assimilation von Radardaten diese Fehler kompensieren und hochwertige Analysen liefern. Der Vorhersagehorizont wird unter beiderlei AB um 6 Stunden erweitert, wobei Skalen bis zu 50 km vorhersagbar bleiben, wenn nur kleinskalige Fehler vorliegen. Wenn synoptische Unsicherheiten auftreten, sind Skalen bis 100 km vorhersagbar. Die Experimente zeigen ferner die gestiegene Vorhersagbarkeit bei Konvektion mit hohem Organisationsgrad. Auch konnten wir diese Ergebnisse im ab Mai 2019 operationellen COSMO-KENDA-System des DWD bestätigen. Des weiteren fanden wir eine höhere Vorhersagbarkeit von Regimen mit synoptischem Einfluss sowie im bergigen Süden Deutschlands verglichen mit dem flacheren Norden. Über drei Sommer hinweg fanden wir in einem älteren DWD-Modell, das Wetterlagen, die mehr von lokalen Prozessen abhängen und weniger vorhersagbar sind als diejenigen, die einem synoptischen Einfluss unterliegen. Beide Wetterlagen zeigen keinen signifikanten Effekt der Orographie

The Influences of Sea-Surface Temperature Uncertainty on Cool-Season High-Shear, Low Cape Severe Weather Event Predictability in the Southeast United States

Environments conducive to severe weather and tornadoes occur throughout the southeastern United States, particularly during the cold-season. Throughout the cold-season, severe weather in this region predominantly occurs in environments characterized by high-shear, low-CAPE (HSLC). An important aspect to the production of severe weather in HSLC environments in the southeast United States is that air parcels that help contribute to the limited positive-buoyancy generation originate over areas such as the Gulf of Mexico, western Caribbean Sea, and western Atlantic Ocean. These relatively warm bodies of water, particularly outside of the cooler coastal shelf regions, allow the air parcels to warm and moisten via latent heat and surface sensible fluxes. It is hypothesized that the forecasts of cold-season severe weather in the southeastern United States are sensitive to the treatment of the underlying ocean surface, which influences the simulated representation of the surface heat exchange between the air and sea. We aimed to address and quantify these sensitivities by conducting numerical simulations for eight identified cold-season southeastern United States severe weather cases initialized using several different sea-surface temperature (SST) analyses. An ensemble of forecasts using varying atmospheric and SST analyses is also conducted for the case with the largest variability in forecast skill between SST initializations to quantify the contributions of initial atmospheric and SST uncertainty to subsequent forecast uncertainty. Neighborhood-based forecast verification techniques based off updraft helicity swaths are used to quantify these uncertainties

PREDICTABILITY OF THE OVERLAND REINTENSIFICATION OF NORTH ATLANTIC TROPICAL CYCLONE ERIN (2007)

Tropical cyclones (TC) typically decrease in intensity upon interacting with land because of increased surface roughness and decreased surface evaporation. However, several studies have documented cases in which TCs maintain their intensity or even intensify over land within non- or weakly baroclinic environments. Yet, our understanding of the precise physical processes that support maintenance or intensification over land in non- or weakly baroclinic environments remains limited, and the predictive skill for these outcomes has yet to be quantified. We begin this process by quantifying the predictive skill and forecast uncertainty of the overland intensification of North Atlantic Tropical Storm Erin in 2007 using a 50-member ensemble of free forecasts initialized from the output of an ensemble adjustment Kalman Filter-based cycled data assimilation system using the Data Assimilation Research Testbed software and Advanced Research Weather Research and Forecasting model. The ensemble outputs are then analyzed using ensemble sensitivity analysis (to provide meaningful physical insight into the relevant forecast sensitivities, even in environments where non-linear processes are important), ensemble subsetting (e.g., strong versus weak TCs), and others, to assess the sensitivity in overland intensity to finite-amplitude atmospheric variability. Additionally, simpler measures such as intensity variability across the ensemble are utilized as part of the analysis, which we compare to both over-water intensification cases and idealized simulations of overland intensity change.We then take different surface and vertical level observation types plot them for both the prior and posterior analyses to compare the observation diagnostics at each analysis time. Optimal performance is achieved when the RMSE and spread are close in magnitude to each other, which could be indicative of well-tuned observation error statistics (Romine et al. 2013). Along with the evaluation of different observation diagnostics, a comprehensive analysis of ensemble outputs is conducted (Figs. 13, 14). Members are categorized into GOOD and BAD members, to help delineate which members best represent Erin’s intensity late on 18 August and the early hours of 19 August 2007 (Figs. 15, 16), which are determined with the help of MSLP, where the good members display at least one closed isobar for an extended period of time in the simulation, and bad members experience near-immediate dissipation

Exploration Of Model-Resolution Dependence Of Forecasted Wind Hazards For Small Unmanned Aircraft System Operations

Use of the small Unmanned Aircraft System (sUAS) for commercial applications is growing. Once approval is granted to conduct flights Beyond Visual Line Of Sight (BVLOS), utilization of the sUAS will accelerate. Hazards associated with sUAS flight, including weather hazards, must be understood when flying BVLOS. One of the leading weather hazards is wind. In this study, nested Weather Research and Forecasting (WRF) model simulations with horizontal grid spacings of 12 km, 4 km, 1.33 km, and 0.444 km were conducted to evaluate the impact changing resolution has on wind fields and, thus, on the forecasting of sUAS wind hazards. The simulated area lies within Central New York (CNY); the surrounding topographic features commonly generate small-scale wind patterns, creating excellent opportunities to explore the dependence of winds on model resolution. Results suggest the importance in increasing model resolution to increase sUAS safety, where a 1.33 km resolution well-identifies hazardous winds. The 0.444 km resolution resolved more detailed atmospheric features at the cost of an increase in computational power. An ensemble model approach along with human interpretation is hypothesized to best facilitate a sUAS-centric forecast

Assessing the Predictability of Convection Initiation Using an Object-Based Approach

Improvements in numerical forecasts of deep, moist convection have been notable in recent years and are in large part due to increased computational power allowing for the explicit numerical representation of convection. Accurately forecasting the timing and location of convection initiation (CI), however, remains a substantial forecast challenge. This is attributed to the inherently limited intrinsic predictability of CI due to its dependence on highly non-linear moist physics and fine-scale atmospheric processes that are poorly represented in observations. Because CI is the starting point of deep, moist convection that grows upscale, even small errors in initial convective development can rapidly spread to larger scales, having potentially significant impacts on downstream forecasts. This study investigates the practical predictability of CI using the Advanced Research Weather Research and Forecasting (WRF-ARW) model with a horizontal grid spacing of 429 meters. A unique object-based method is used to evaluate very high-resolution model performance for twenty-five cases of CI across the west-central High Plains of the United States from the 2010 convective season. CI objects are defined as areas of higher observed or model simulated radar reflectivity that develop and remain sustained for a sufficient period of time. Model simulations demonstrate an average probability of detection of 0.835, but due to significant overproduction of CI, an average false alarm ratio of 0.664 and bias ratio of 2.49. The average critical success index through all simulations is 0.315. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 7 minutes and distance errors of around 62 kilometers. The operational utility and inherent biases of such high-resolution simulations are discussed

Spatio-temporal visual analytics: a vision for 2020s

Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. Visual analytics develops methods, analytical workflows, and software tools for analysing data of various types, particularly, spatio-temporal data, which can describe the processes going on in the environment, society, and economy. We briefly overview the achievements of the visual analytics research concerning spatio-temporal data analysis and discuss the major open problems

An Assessment of the Subseasonal Predictability of Severe Thunderstorm title Environments and Activity using the Climate Forecast System Version 2

The prospect for skillful long-term predictions of atmospheric conditions known to directly contribute to the onset and maintenance of severe convective storms remains unclear. A thorough assessment of the capability for a global climate model such as the Climate Forecast System Version 2 (CFSv2) to skillfully represent parameters related to severe weather has the potential to significantly improve medium- to longrange outlooks vital to risk managers. Environmental convective available potential energy (CAPE) and deep-layer vertical wind shear (DLS) can be used to distinguish an atmosphere conducive to severe storms from one supportive of primarily nonsevere ordinary convection. As such, this research concentrates on the predictability of CAPE, DLS, and a product of the two parameters (CAPEDLS) by the CFSv2 with a specific focus on the subseasonal timescale. Individual month-long verification periods from the Climate Forecast System reanalysis (CFSR) dataset are measured against a climatological standard using cumulative distribution function (CDF) and area-under-the-CDF (AUCDF) techniques designed mitigate inherent model biases while concurrently assessing the entire distribution of a given parameter in lieu of a threshold-based approach. Similar methods imposed upon the CFS reforecast (CFSRef) and operational CFSv2 allow for comparisons elucidating both spatial and temporal trends in skill using correlation coefficients, proportion correct metrics, Heidke skill score (HSS), and root-meansquare- error (RMSE) statistics. Key results show the CFSv2-based output often demonstrates skill beyond a climatologically-based threshold when the forecast is notably anomalous from the 29-year (1982-2010) mean CFSRef prediction (exceeding one standard deviation at grid point level). CFSRef analysis indicates enhanced skill during the months of April and June (relative to May) and for predictions of DLS. Furthermore, years exhibiting skill in terms of RMSE are shown to possess certain correlations with El Ni˜no-Southern Oscillation conditions from the preceding winter and concurrent Madden Julian Oscillation activity. Applying results gleaned from the CFSRef analysis to the operational CFSv2 (2011-16) indicates predictive skill can be increased by isolating forecasts meeting multiple parameter-based relationships