scholarly journals Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 177 ◽  
Author(s):  
Keith Hutchison ◽  
Barbara Iisager
Keyword(s):  
Satellite Imagery ◽  
Numerical Weather Prediction ◽  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Cloud Model ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Spectral Bands ◽  
Cloud Forecast

Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and climate models. A possible explanation for this apparent omission involves the difficulty in developing cloud ground truth databases for the verification of large-scale numerical simulations. Therefore, the process of developing highly accurate cloud cover fraction truth data from manually generated cloud/no-cloud analyses of multispectral satellite imagery is the focus of this article. The procedures exploit the phenomenology to maximize cloud signatures in a variety of remotely sensed satellite spectral bands in order to create accurate binary cloud/no-cloud analyses. These manual analyses become cloud cover fraction truth after being mapped to the grids of the target datasets. The process is demonstrated by examining all clouds in a NAM dataset along with a 24 h WRF cloud forecast field generated from them. Quantitative comparisons with the cloud truth data for the case study show that clouds in the NAM data are under-specified while the WRF model greatly over-predicts them. It is concluded that highly accurate cloud cover truth data are valuable for assessing cloud model input and output datasets and their creation requires the collection of satellite imagery in a minimum set of spectral bands. It is advocated that these remote sensing requirements be considered for inclusion into the designs of future environmental satellite systems.

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

Atmospheric angular momentum forecasts as novel tests of global numerical weather prediction models

1991 ◽  
Vol 334 (1633) ◽  
pp. 55-92 ◽  
Keyword(s):  
Angular Momentum ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Angular Momentum ◽  
Axial Component ◽  
Practical Applications ◽  
Numerical Weather ◽  
Three Components

The global circulation of the terrestrial atmosphere exhibits fluctuations of considerable amplitude in all three components of its total angular momentum on interannual, seasonal and shorter timescales. The fluctuations must be intimately linked with nonlinear barotropic and baroclinic energetic conversion processes throughout the whole atmosphere and it is advocated that studies of routinely produced determinations of atmospheric angular momentum (AAM) changes be incorporated into systematic diagnostic investigations of large-scale atmospheric flows, AAM fluctuations are generated by dynamical interactions between the atmosphere and the underlying planet. These excite tiny but measurable compensating fluctuations in the rotation vector of the massive solid Earth, thereby ensuring conservation of the angular momentum of the whole system. Forecasts and analyses of changes in AAM from the output of a global numerical weather prediction (GNWP) model constitute a stringent test of the model. Successful forecasts of the axial com ponent of AAM, and hence of irregular non-tidal components of short-term changes in the Earth’s rotation, would find practical applications in various areas of astronomy and geodesy, such as spacecraft navigation. Reported in this paper are the main findings of intercomparisons of analyses and forecasts of changes in all three components of AAM obtained from the operational GNWP models at the United Kingdom Meteorological Office (UKMO) and the European Centre for Medium Range Weather Forecasts (ECMWF), over the period covering the two years 1987 and 1988. Included in the results obtained is the finding that useful forecasts of changes in the axial component of AAM can be made out to 5 days and even slightly longer.

scholarly journals The Plant–Craig Stochastic Convection Scheme in ICON and Its Scale Adaptivity

2014 ◽  
Vol 71 (9) ◽  
pp. 3404-3415 ◽  
Author(s):  
Richard J. Keane ◽  
George C. Craig ◽  
Christian Keil ◽  
Günther Zängl
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Precipitation Variability ◽  
Convection Scheme ◽  
Numerical Weather ◽  
Multiple Resolutions ◽  
Input Variables

Abstract The emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.

scholarly journals EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian M. Grams
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Sets ◽  
Forecast Uncertainty ◽  
Numerical Weather ◽  
Process Oriented ◽  
Air Streams

Abstract. Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending air streams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within two days. This requires numerical data with high spatial and temporal resolution which is often not available from standard output and requires expensive computations. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models' skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and of their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective.

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

scholarly journals Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 489 ◽  
Author(s):  
Jürgen Helmert ◽  
Aynur Şensoy Şorman ◽  
Rodolfo Alvarado Montero ◽  
Carlo De Michele ◽  
Patricia de Rosnay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Availability ◽  
Observation Data ◽  
Climate Change Scenarios ◽  
Numerical Weather ◽  
The Future

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

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