scholarly journals Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts

2011 ◽  
Vol 138 (664) ◽  
pp. 755-768 ◽  
Author(s):  
K. Kober ◽  
G. C. Craig ◽  
C. Keil ◽  
A. Dörnbrack
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Convective Precipitation ◽  
Numerical Weather

scholarly journals Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State

2011 ◽  
Vol 26 (6) ◽  
pp. 785-807 ◽  
Author(s):  
Jonathan L. Case ◽  
Sujay V. Kumar ◽  
Jayanthi Srikishen ◽  
Gary J. Jedlovec
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Southeastern United States ◽  
Model Verification ◽  
Sea Surface ◽  
Convective Precipitation ◽  
Numerical Weather

Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

Physics, Resolution and Data Assimilation: Making sense of Greenland climate and ice sheet Surface Mass Balance with HARMONIE Climate

2021 ◽  
Author(s):  
Ruth Mottram ◽  
Oskar Landgren ◽  
Rasmus Anker Pedersen ◽  
Kristian Pagh Nielsen ◽  
Ole Bøssing Christensen ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Climate Model ◽  
Weather Prediction ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Mass ◽  
Mass Budget ◽  
Numerical Weather

<p>The development of the HARMONIE model system has led to huge advances in numerical weather prediction, including over Greenland where a numerical weather prediction (NWP) model is used to forecast daily surface mass budget over the Greenland ice sheet as presented on polarportal.dk. The new high resolution Copernicus Arctic Reanalysis further developed the possibilities in HARMONIE with full 3DVar data assimilation and extended use of quality-controlled local observations. Here, we discuss the development and current status of the climate version of the HARMONIE Climate model (HCLIM). The HCLIM system has opened up the possibility for flexible use of the model at a range of spatial scales using different physical schemes including HARMONIE-AROME, ALADIN and ALARO for different spatial and temporal resolutions and assimilating observations, including satellite data on sea ice concentration from ESA CCI+, to improve hindcasts. However, the range of possibilities means that documenting the effects of different physics and parameterisation schemes is important before widespread application. </p><p>Here, we focus on HCLIM performance over the Greenland ice sheet, using observations to verify the different plausible set-ups and investigate biases in climate model outputs that affect the surface mass budget (SMB) of the Greenland ice sheet. </p><p>The recently funded Horizon 2020 project PolarRES will use the HCLIM model for very high resolution regional downscaling, together with other regional climate models in both Arctic and Antarctic regions, and our analysis thus helps to optimise the use of HCLIM in the polar regions for different modelling purposes.</p>

Objective 3D atmospheric front detection in high-resolution numerical weather prediction data

2021 ◽  
Author(s):  
Andreas Beckert ◽  
Lea Eisenstein ◽  
Tim Hewson ◽  
George C. Craig ◽  
Marc Rautenhaus
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Feature Detection ◽  
3D Visualization ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Detection Methods ◽  
Numerical Weather ◽  
Atmospheric Fronts ◽  
Front Detection

<p><span>Atmospheric fronts, a widely used conceptual model in meteorology, describe sharp boundaries between two air masses of different thermal properties. In the mid-latitudes, these sharp boundaries are commonly associated with extratropical cyclones. The passage of a frontal system is accompanied by significant weather changes, and therefore fronts are of particular interest in weather forecasting. Over the past decades, several two-dimensional, horizontal feature detection methods to objectively identify atmospheric fronts in numerical weather prediction (NWP) data were proposed in the literature (e.g. Hewson, Met.Apps. 1998). In addition, recent research (Kern et al., IEEE Trans. Visual. Comput. Graphics, 2019) has shown the feasibility of detecting atmospheric fronts as three-dimensional surfaces representing the full 3D frontal structure. In our work, we build on the studies by Hewson (1998) and Kern et al. (2019) to make front detection usable for forecasting purposes in an interactive 3D visualization environment. We consider the following aspects: (a) As NWP models evolved in recent years to resolve atmospheric processes on scales far smaller than the scale of midlatitude-cyclone- fronts, we evaluate whether previously developed detection methods are still capable to detect fronts in current high-resolution NWP data. (b) We present integration of our implementation into the open-source “Met.3D” software (http://met3d.wavestoweather.de) and analyze two- and three-dimensional frontal structures in selected cases of European winter storms, comparing different models and model resolution. (c) The considered front detection methods rely on threshold parameters, which mostly refer to the magnitude of the thermal gradient within the adjacent frontal zone - the frontal strength. If the frontal strength exceeds the threshold, a so-called feature candidate is classified as a front, while others are discarded. If a single, fixed, threshold is used, unwanted “holes” can be observed in the detected fronts. Hence, we use transparency mapping with fuzzy thresholds to generate continuous frontal features. We pay particular attention to the adjustment of filter thresholds and evaluate the dependence of thresholds and resolution of the underlying data.</span></p>

Evaluation of a high-resolution numerical weather prediction model's simulated clouds using observations from CloudSat, GOES-13 and in situ aircraft

2018 ◽  
Vol 144 (715) ◽  
pp. 1681-1694 ◽  
Author(s):  
Zhipeng Qu ◽  
Howard W. Barker ◽  
Alexei V. Korolev ◽  
Jason A. Milbrandt ◽  
Ivan Heckman ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather

The Common Community Physics Package (CCPP): bridging the gap between research and operations to improve U.S. numerical weather prediction capabilities

2020 ◽  
Author(s):  
Dom Heinzeller ◽  
Grant Firl ◽  
Ligia Bernardet ◽  
Laurie Carson ◽  
Man Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Modeling Framework ◽  
Initial Development ◽  
Numerical Weather ◽  
Computational Performance ◽  
The Common ◽  
The U.S ◽  
Physical Parameterizations

<p>Improving numerical weather prediction systems depends critically on the ability to transition innovations from research to operations (R2O) and to provide feedback from operations to research (O2R). This R2O2R cycle, sometimes referred to as "crossing the valley of death", has long been identified as a major challenge for the U.S. weather enterprise.</p><p>As part of a broader effort to bridge this gap and advance U.S. weather prediction capabilities, the Developmental Testbed Center (DTC) with staff at NOAA and NCAR has developed the Common Community Physics Package (CCPP) for application in NOAA's Unified Forecasting System (UFS). The CCPP consists of a library of physical parameterizations and a framework, which interfaces the physics with atmospheric models based on metadata information and standardized interfaces. The CCPP physics library contains physical parameterizations from the current operational U.S. global, mesoscale and high-resolution models, future implementation candidates, and additional physics from NOAA, NCAR and other organizations. The range of physics options in the CCPP physics library enables the application of the UFS - as well as every other model using the CCPP - across scales, from now-casting to seasonal and from high-resolution regional to global ensembles.</p><p>While the initial development of the CCPP was centered around the FV3 (Finite-Volume Cubed-Sphere) dynamical core of the UFS, its focus has since widened. The CCPP is also used by the DTC Single Column Model to support a hierarchical testing strategy, and by the next generation NEPTUNE (Navy Environmental Prediction sysTem Utilizing the Numa corE) model of the Naval Research Laboratory. Further, and most importantly, NOAA and NCAR recently signed an agreement to jointly develop the CCPP framework as a single, standardized way to interface physics with their models of the atmosphere (and other compartments of the Earth system). This places the CCPP in the heart of several of the U.S. flagship models and opens the door for bringing innovations from a large research community into operations.</p><p>In this contribution, we will present a brief overview of the concept of the CCPP, its technical design and the requirements for parameterizations to be considered as CCPP-compliant. We will describe the integration of CCPP in the UFS and touch upon the challenges in creating a flexible modeling framework while maintaining high computational performance. We will also provide information on how to obtain, use and contribute to the CCPP, as well as on the future development of the CCPP framework and upcoming additions to the CCPP physics library.</p>

Sign in / Sign up

Export Citation Format

Share Document