Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

<p>Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.</p>

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

scholarly journals Water vapour foreign-continuum absorption in near-infrared windows from laboratory measurements

2012 ◽  
Vol 370 (1968) ◽  
pp. 2557-2577 ◽  
Author(s):  
Igor V. Ptashnik ◽  
Robert A. McPheat ◽  
Keith P. Shine ◽  
Kevin M. Smith ◽  
R. Gary Williams
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Pair Interaction ◽  
Atmospheric Conditions ◽  
Laboratory Measurements ◽  
Atmospheric Absorption ◽  
Clear Sky ◽  
Long Time

For a long time, it has been believed that atmospheric absorption of radiation within wavelength regions of relatively high infrared transmittance (so-called ‘windows’) was dominated by the water vapour self-continuum, that is, spectrally smooth absorption caused by H 2 O−H 2 O pair interaction. Absorption due to the foreign continuum (i.e. caused mostly by H 2 O−N 2 bimolecular absorption in the Earth's atmosphere) was considered to be negligible in the windows. We report new retrievals of the water vapour foreign continuum from high-resolution laboratory measurements at temperatures between 350 and 430 K in four near-infrared windows between 1.1 and 5 μm (9000–2000 cm −1 ). Our results indicate that the foreign continuum in these windows has a very weak temperature dependence and is typically between one and two orders of magnitude stronger than that given in representations of the continuum currently used in many climate and weather prediction models. This indicates that absorption owing to the foreign continuum may be comparable to the self-continuum under atmospheric conditions in the investigated windows. The calculated global-average clear-sky atmospheric absorption of solar radiation is increased by approximately 0.46 W m −2 (or 0.6% of the total clear-sky absorption) by using these new measurements when compared with calculations applying the widely used MTCKD (Mlawer–Tobin–Clough–Kneizys–Davies) foreign-continuum model.

scholarly journals The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

2012 ◽  
Vol 140 (8) ◽  
pp. 2689-2705 ◽  
Author(s):  
Marc Berenguer ◽  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Diurnal Cycle ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Model Performance ◽  
Radar Data ◽  
Skill Scores ◽  
Diurnal Cycle Of Precipitation ◽  
Timing Of Initiation ◽  
Short Term Forecasting

Abstract This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting. The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar. Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill. The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 103°–85°W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

scholarly journals Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

2017 ◽  
Vol 32 (5) ◽  
pp. 1819-1840 ◽  
Author(s):  
David John Gagne ◽  
Amy McGovern ◽  
Sue Ellen Haupt ◽  
Ryan A. Sobash ◽  
John K. Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Size Distribution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Object Identification ◽  
Atmospheric Conditions ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Abstract Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

scholarly journals Diurnal Valley Winds in a Deep Alpine Valley: Observations

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 54
Author(s):  
Fabienne Schmid ◽  
Juerg Schmidli ◽  
Maxime Hervo ◽  
Alexander Haefele
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Warm Season ◽  
Ground Level ◽  
Weather Conditions ◽  
Wind Profiler ◽  
Fair Weather ◽  
Wind Maximum ◽  
Alpine Valley

Diurnal valley winds frequently form over complex topography, particularly under fair weather conditions, and have a significant impact on the local weather and climate. Since diurnal valley winds result from complex and multi-scale interactions, their representation in numerical weather prediction models is challenging. Better understanding of these local winds based on observations is crucial to improve the accuracy of the forecasts. This study investigates the diurnal evolution of the three-dimensional mean wind structure in a deep Alpine valley, the Rhone valley at Sion, using data from a radar wind profiler and a surface weather station operated continuously from 1 September 2016 to 17 July 2017. In particular, the wind profiler data was analyzed for a subset of days on which fair weather conditions allowed for the full development of thermally driven winds. A pronounced diurnal cycle of the wind speed, as well as a reversal of the wind direction twice per day is documented for altitudes up to about 2 km above ground level (AGL) in the warm season and less than 1 km AGL in winter. The diurnal pattern undergoes significant changes during the course of the year. Particularly during the warm-weather months of May through to September, a low-level wind maximum occurs, where mean maximum up-valley velocities of 8–10 m s−1 are found between 15–16 UTC at altitudes around 200 m AGL. In addition, during nighttime, a down-valley jet with maximum wind speeds of 4–8 m s−1 around 1 km AGL is found. A case study of a three-day period in September 2016 illustrates the occurrence of an elevated layer of cross-valley flow around 1–1.5 km AGL.

scholarly journals Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects

2018 ◽  
Vol 33 (6) ◽  
pp. 1681-1708 ◽  
Author(s):  
Thomas A. Jones ◽  
Patrick Skinner ◽  
Kent Knopfmeier ◽  
Edward Mansell ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Prediction Models ◽  
Negative Impact ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Variable Density ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Double Moment

AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

scholarly journals Design and implementation of weather fore casting system based on cloud computing and data mining techniques

2018 ◽  
Vol 7 (2.7) ◽  
pp. 219
Author(s):  
Rajarajeswari P ◽  
J Pradeep kumar ◽  
D Vasumathi
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Decision Tree ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Data Mining Techniques ◽  
Design And Implementation ◽  
Casting System

Software engineering is an engineering discipline which is used to analyze, modeling and development of the system. Normally Weather conditions are changing continuously with respective to atmospheric conditions. In the phase of modeling of the Weather fore casting system is performed by taking the data which is collected on atmospheric conditions and record temperature, rainfall, humidity and other parameters. The main objective of this paper is focused on Design and implementation of Weather forecasting system. Weather forecasting model is prepared by using Cloud computing and Data mining techniques. This System is analyzed by using temperature, rainfall conditions. Statistical framework is provided on Modeling for surface process of weather forecasting with Data assimilation. This paper presents the Conceptual level architecture of Weather prediction model by using decision tree. Proposed algorithm is used for the construction of decision tree. Weather forecasting report is implemented by using Android.

scholarly journals Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

2016 ◽  
Vol 31 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Michaël Zamo ◽  
Liliane Bel ◽  
Olivier Mestre ◽  
Joël Stein
Keyword(s):  
Wind Speed ◽  
Random Forests ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Near Surface ◽  
Numerical Weather ◽  
Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

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