Review Lecture: Local weather forecasting

1980 ◽  
Vol 371 (1745) ◽  
pp. 179-211 ◽  
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Quarter Century ◽  
New Methods ◽  
Weather Observations ◽  
Wide Range ◽  
Local Weather ◽  
Improved Methods ◽  
Made In

Good progress has been made in forecasting the broad pattern of the weather for periods up to a few days ahead as a result of developments in mathematical weather prediction models. However, our ability to forecast, just a few hours ahead, the detailed weather pattern for specific locations, still leaves a lot to be desired. In this lecture it is suggested that local forecasting, after a quarter century of stagnation, is poised for a decade of slow but steady improvement. Changes will come as a result of new programs which exploit and integrate advances in several areas. These areas include better weather observations, especially from satellites and radar, improved methods of data processing and analysis, and the development of more detailed numerical prediction models. New methods of communications, such as viewdata, will enable the improved local forecasts to be disseminated promptly and in a wide range of specially tailored formats. This will contribute to the development of a more user-oriented meteorological service.

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 Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection

2007 ◽  
Vol 64 (11) ◽  
pp. 3737-3741 ◽  
Author(s):  
Ronald M. Errico ◽  
George Ohring ◽  
Fuzhong Weng ◽  
Peter Bauer ◽  
Brad Ferrier ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Forecasting ◽  
International Workshop ◽  
Weather Prediction ◽  
Satellite Observations ◽  
Atmospheric Sciences ◽  
Numerical Weather ◽  
Special Collection ◽  
Numerical Weather Prediction Models

Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.

15 year re-analysis of the urban climate of Amsterdam using WRF

2020 ◽  
Author(s):  
Sytse Koopmans ◽  
Gert-Jan Steeneveld ◽  
Ronald van Haren ◽  
Albert Holtslag
Keyword(s):  
Data Assimilation ◽  
The Netherlands ◽  
Thermal Comfort ◽  
Prediction Models ◽  
Weather Prediction ◽  
Urban Climate ◽  
Urban Canopy ◽  
Human Thermal Comfort ◽  
Weather Observations ◽  
Simulated Precipitation

<p><strong>15 year re-analysis of the urban climate of Amsterdam using WRF </strong></p><p><strong> </strong></p><p>Sytse Koopmans<sup>1</sup> ([email protected]), Gert-Jan Steeneveld<sup>1</sup>, Ronald van Haren<sup>2</sup>, Albert A.M. Holtslag<sup>1</sup>.</p><p> </p><p><sup>1</sup> Wageningen University and Research, the Netherlands:</p><p><sup>2 </sup>Netherlands eScience Center, the Netherlands:</p><p> </p><p> </p><p>Ongoing world-wide climate change and urbanization illustrate the need to understand urban hydrometeorology and its implications for human thermal comfort and water management. Numerical weather prediction models can assist to understand these issues, as they progress increasingly towards finer scales. With high model resolutions (grid spacing of 100m), effective representation of cities becomes crucial. The complex structures of cities, configuration of buildings, streets and scattered vegetation, require a different modelling approach than the homogeneous rural surroundings. The current urban canopy-layer schemes account for these city specific characteristics, but differ substantially amongst each other due to uncertainty in land use parameters and incomplete physical understanding. Therefore, the hindcasting of the urban environment needs improvement.</p><p>In this study, we improve the WRF (Weather Research and Forecasting) mesoscale model performance by incorporating observations of a variety of sources using data assimilation (WRF-3DVAR) and nudging techniques on a resolution up to 167 meter. Data assimilation aims to accurately describe the most probable atmospheric state by steering the model fields in the direction of the observations. Specific to urban boundary layers, a novel approach has been developed to nudge modelled urban canyon temperatures with quality controlled urban weather observations. Adjusting the urban fabric accordingly is crucial, because of the large heat storage within urban canopies. The road and wall layers of the urban canopy are adjusted depending on the bulk heat transfer coefficient and urban geometry. Other data assimilation sources consists of WMO synoptic weather observations and volume radar data.</p><p>The results of the 15-year climatological urban re-analysis are here presented and it is subdivided in three key questions. First, we attempt to answer how large the trends are in human thermal comfort over the 15 year period. Second, we investigate if there are seasonality’s detected in maximum urban heat island intensities. Earlier found hysteresis-like curves were reproduced to a large extent for for pedestrian level air temperatures. Lastly, we analyse trends in extreme precipitation using simulated precipitation data on one second interval.</p>

scholarly journals Data Assimilation as Synchronization of Truth and Model: Experiments with the Three-Variable Lorenz System*

2006 ◽  
Vol 63 (9) ◽  
pp. 2340-2354 ◽  
Author(s):  
Shu-Chih Yang ◽  
Debra Baker ◽  
Hong Li ◽  
Katy Cordes ◽  
Morgan Huff ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Chaos Synchronization ◽  
Prediction Models ◽  
Partial Information ◽  
Coupling Parameter ◽  
Weather Prediction ◽  
Adaptive Synchronization ◽  
Accurate Analysis ◽  
Wide Range ◽  
Bred Vectors

Abstract The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents “truth” to another that represents “the model.” By adding realistic “noise” to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization–data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

scholarly journals DREAMTools: a Python package for scoring collaborative challenges

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1030 ◽  
Author(s):  
Thomas Cokelaer ◽  
Mukesh Bansal ◽  
Christopher Bare ◽  
Erhan Bilal ◽  
Brian M. Bot ◽  
...  
Keyword(s):  
Computational Methods ◽  
Training Data ◽  
Model Parameters ◽  
Automated Scoring ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
New Methods ◽  
Wide Range ◽  
Improved Methods ◽  
Python Package

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of September 2015, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools.

Benefits of the Advanced Baseline Imager (ABI) for Ensemble-Based Analysis and Prediction of Severe Thunderstorms

2020 ◽  
Author(s):  
Yunji Zhang ◽  
David J. Stensrud ◽  
Eugene E. Clothiaux
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Sampling Errors ◽  
Severe Thunderstorm ◽  
Clear Sky ◽  
Severe Thunderstorms ◽  
Challenges And Opportunities ◽  
Open Question ◽  
Numerical Weather Prediction Models

AbstractRecent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager – finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum – contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a byproduct of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting.

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