Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

scholarly journals Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

2014 ◽  
Vol 1 (1) ◽  
pp. 917-952
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California

2020 ◽  
Vol 148 (8) ◽  
pp. 3489-3506
Author(s):  
Michael Scheuerer ◽  
Matthew B. Switanek ◽  
Rochelle P. Worsnop ◽  
Thomas M. Hamill
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.

scholarly journals Flood forecasting using medium-range probabilistic weather prediction

2005 ◽  
Vol 9 (4) ◽  
pp. 365-380 ◽  
Author(s):  
B. T. Gouweleeuw ◽  
J. Thielen ◽  
G. Franchello ◽  
A. P. J. De Roo ◽  
R. Buizza
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Probabilistic Approach ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Ensemble Techniques ◽  
Medium Range ◽  
Prediction Systems ◽  
Nwp Model

Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.

scholarly journals Short-Range and Medium-Range Weather Forecasting in the Extratropics during Wintertime with and without an Interactive Ocean

2006 ◽  
Vol 134 (7) ◽  
pp. 1972-1986 ◽  
Author(s):  
Thomas Jung ◽  
Frederic Vitart
Keyword(s):  
Northern Hemisphere ◽  
Short Range ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Coupled System ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Significant Difference ◽  
Medium Range ◽  
The Impact

Abstract The ECMWF monthly forecasting system is used to investigate the impact that an interactive ocean has on short-range and medium-range weather predictions in the Northern Hemisphere extratropics during wintertime. On a hemispheric scale the predictive skill for mean sea level pressure (MSLP) with and without an interactive ocean is comparable. This can be explained by the relatively small impact that coupling has on MSLP forecasts. In fact, deterministic and ensemble integrations reveal that the magnitude of forecast error and the perturbation growth due to analysis uncertainties, respectively, by far outweigh MSLP differences between coupled and uncoupled integrations. Furthermore, no significant difference of the ensemble spread between the uncoupled and coupled system is found. The authors’ conclusions apply equally for a number of cases of rapidly intensifying extratropical cyclones in the North Atlantic region. Further experimentation with different atmospheric model versions, different horizontal atmospheric resolutions, and different ocean model formulation reveals the robustness of the findings. The results suggest that (for the cases, resolutions, and model complexities considered is this study) the benefit of using coupled atmosphere–ocean models to carry out 1–10-day MSLP forecasts is relatively small, at least in the Northern Hemisphere extratropics during wintertime.

An Assessment of the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF

2020 ◽  
Author(s):  
Michael P. Rennie ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Forecast Errors ◽  
Horizontal Line ◽  
Numerical Weather ◽  
Medium Range ◽  
Ecmwf Model ◽  
The Impact

<p>The European Space Agency’s Aeolus mission, which was launched in August 2018, provides profiles of horizontal line-of-sight (HLOS) wind observations from a polar orbiting satellite.  The European Centre For Medium-Range Weather Forecasts (ECMWF) began the operational assimilation of Aeolus Level-2B winds on 9 January 2020 in their global NWP (Numerical Weather Prediction) model, 1 year and 4 months after the first Level-2B wind products were produced in near real time via ESA’s ground processing segment.  This achievement was possible because of the production of good data quality, which was met through a close collaboration of all the parties involved within the Aeolus Data Innovation and Science Cluster (DISC) and via the great efforts of ESA, industry and ground processing algorithms pre- and post-launch.<br>Through the careful assessment of the statistics of differences of the Aeolus winds relative to the ECMWF model, the Level-2B Rayleigh winds were found to have large systematic errors.  The systematic errors were found to be highly correlated with ALADIN’s (Atmospheric Laser Doppler Instrument) primary mirror temperatures, which vary in a complex manner due to the variation in Earthshine and thermal control of the mirror.  The correction of this source of bias in the ground processing is underway, therefore in the meantime a bias correction scheme using the ECMWF model as a reference was developed for successful data assimilation; the scheme will be described.  <br>We will present the results of the Aeolus NWP impact assessment which led to the decision to go operational.  Aeolus’ second laser (FM-B, available since late June 2019) provides statistically significant positive impact of moderate to large amplitude, of similar magnitude to some other important and well-established observing systems (such as IR radiances, GNNS radio occultation and Atmospheric Motion Vectors).  Observing System Experiments demonstrate reduction of forecast errors in geopotential and vector wind of around 2% in the tropics and 2-3% in the southern hemisphere for short-range and medium range forecasts (up to day 10).  This positive impact is particularly impressive given that Aeolus provides less than 1% of the total number of observations assimilated, showing the value of direct wind observations for global NWP.</p>

scholarly journals Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130290 ◽  
Author(s):  
Antje Weisheimer ◽  
Susanna Corti ◽  
Tim Palmer ◽  
Frederic Vitart
Keyword(s):  
Time Scales ◽  
Weather Prediction ◽  
Seasonal Forecasting ◽  
Near Surface ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact ◽  
Stochastically Perturbed

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region.

scholarly journals The Stratospheric Extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and Its Impact on Tropospheric Forecasts

2012 ◽  
Vol 140 (6) ◽  
pp. 1924-1944 ◽  
Author(s):  
Martin Charron ◽  
Saroja Polavarapu ◽  
Mark Buehner ◽  
P. A. Vaillancourt ◽  
Cécilien Charette ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Medium Range ◽  
Summer Hemisphere ◽  
Forecasting System ◽  
Winter Hemisphere ◽  
Radiation Scheme

Abstract A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.

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