scholarly journals Performance of Hourly Operational Consensus Forecasts (OCFs) in the Australian Region

2007 ◽  
Vol 22 (6) ◽  
pp. 1345-1359 ◽  
Author(s):  
Chermelle Engel ◽  
Elizabeth Ebert
Keyword(s):  
Wind Speed ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Weather Prediction ◽  
Weighted Average ◽  
Barometric Pressure ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Dewpoint Temperature ◽  
Statistical Correction

Abstract This paper presents an extension of the operational consensus forecast (OCF) method, which performs a statistical correction of model output at sites followed by weighted average consensus on a daily basis. Numerical weather prediction (NWP) model forecasts are received from international centers at various temporal resolutions. As such, in order to extend the OCF methodology to hourly temporal resolution, a method is described that blends multiple models regardless of their temporal resolution. The hourly OCF approach is used to generate forecasts of 2-m air temperature, dewpoint temperature, RH, mean sea level pressure derived from the barometric pressure at the station location (QNH), along with 10-m wind speed and direction for 283 Australian sites. In comparison to a finescale hourly regional model, the hourly OCF process results in reductions in average mean square error of 47% (air temperature), 40% (dewpoint temperature), 43% (RH), 29% (QNH), 42% (wind speed), and 11% (wind direction) during February–March with slightly higher reductions in May. As part of the development of the approach, the systematic and random natures of hourly NWP forecast errors are evaluated and found to vary with forecast hour, with a diurnal modulation overlaying the normal error growth with time. The site-based statistical correction of the model forecasts is found to include simple statistical downscaling. As such, the method is found to be most appropriate for meteorological variables that vary systematically with spatial resolution.

The Influence of Weather On Incubation Scheduling of the White-Rumped Sandpiper (Calidris Fuscicollis): a Uniparental Incubator in a Cold Environment

Behaviour ◽  
1985 ◽  
Vol 95 (3-4) ◽  
pp. 261-289 ◽  
Author(s):  
Robert D. Montgomerie ◽  
Ralph V. Cantar
Keyword(s):  
Circadian Rhythm ◽  
Wind Speed ◽  
Air Temperature ◽  
Weather Conditions ◽  
Barometric Pressure ◽  
High Arctic ◽  
Collective Effects ◽  
Incubation Behaviour ◽  
The Individual

AbstractWe studied the incubation scheduling of 8 white-rumped sandpipers (Calidris fuscicollis), a species in which only the female incubates. Because the female is small and nests in the high arctic, these birds are probably under more cold stress than birds nesting in the temperate zone. We examined the individual and collective effects of several weather variables on a female's incubation behaviour to ascertain what amount of the variability within a day was directly attributable to weather conditions. Birds made an average of 25.1 off-nest trips each day, averaging 10.5 min each. This resulted in spending, on average, 82.5% of their time incubating eggs. There was a clear within-day cycle in incubation scheduling; birds made more and longer trips in the middle of the day and, as a result, spent more total time off the nest in that period. Birds adjusted their hour-by-hour schedules to weather largely by altering the number of trips made, and less so by adjusting trip length. There was a circadian rhythm in recess time/h, explaining at least 11% of the variation in recess time/h. When the circadian rhythm was controlled statistically, weather accounted for an average of 38% of the explainable variation in recess time/h. The relative importance of each weather variable on the recess time/h was (in descending order of importance): wind speed, air temperature, solar radiation, barometric pressure, and relative humidity. Weather (primarily wind speed and temperature) exerted its strongest effects early and late in the bird's active day (0400-2300 h). On cold and windy days, birds increased the time spent on their nests early and late in the day, and made more trips than usual in the middle of the day, when air temperature was highest. We suggest that the incubation scheduling of these birds conformed to the long-term predictability of the daily weather cycle by following a circadian rhythm of behaviour modified by a response to concurrent weather that would have reduced egg cooling.

Gridded Operational Consensus Forecasts of 2-m Temperature over Australia

2012 ◽  
Vol 27 (2) ◽  
pp. 301-322 ◽  
Author(s):  
Chermelle Engel ◽  
Elizabeth E. Ebert
Keyword(s):  
Bias Correction ◽  
Statistical Approach ◽  
Weather Prediction ◽  
Weighted Average ◽  
Numerical Weather Prediction Model ◽  
Small Scale ◽  
Coarse Scale ◽  
Consensus Forecasts ◽  
Average Consensus ◽  
Almost All

Abstract This paper describes an extension of an operational consensus forecasting (OCF) scheme from site forecasts to gridded forecasts. OCF is a multimodel consensus scheme including bias correction and weighting. Bias correction and weighting are done on a scale common to almost all multimodel inputs (1.25°), which are then downscaled using a statistical approach to an approximately 5-km-resolution grid. Local and international numerical weather prediction model inputs are found to have coarse scale biases that respond to simple bias correction, with the weighted average consensus at 1.25° outperforming all models at that scale. Statistical downscaling is found to remove the systematic representativeness error when downscaling from 1.25° to 5 km, though it cannot resolve scale differences associated with transient small-scale weather.

scholarly journals Experience of using the Kalman filter to correct numerical forecasts of surface air temperature

2020 ◽  
Vol 4 ◽  
pp. 28-42
Author(s):  
Yu.V. Alferov . ◽  
◽  
E.G. Klimova ◽  
Keyword(s):  
Kalman Filter ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
One Dimensional ◽  
Numerical Weather ◽  
Statistical Correction ◽  
The One

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

scholarly journals Improvements in Hurricane Intensity Forecasts from a Multimodel Superensemble Utilizing a Generalized Neural Network Technique

2018 ◽  
Vol 33 (3) ◽  
pp. 873-885 ◽  
Author(s):  
T. Ghosh ◽  
T. N. Krishnamurti
Keyword(s):  
Neural Network ◽  
Weather Prediction ◽  
Emergency Evacuation ◽  
Tropical Storm ◽  
Generalized Regression Neural Network ◽  
Forecast Errors ◽  
Dynamical Models ◽  
Consensus Forecasts ◽  
Ensemble Mean ◽  
Generalized Regression

Abstract Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.

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.

scholarly journals Analysis and Application of Sheppard’s Airflow Model to Predict Mechanical Orographic Lifting and the Occurrence of Mountain Clouds

2006 ◽  
Vol 45 (10) ◽  
pp. 1376-1387 ◽  
Author(s):  
Jan Kleissl ◽  
Richard E. Honrath ◽  
Diamantino V. Henriques
Keyword(s):  
Water Vapor ◽  
Wind Speed ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Relative Increase ◽  
Horizontal Wind ◽  
Forecast Errors ◽  
Horizontal Wind Speed ◽  
Pollution Dispersion ◽  
Numerical Weather

Abstract Mechanically driven orographic lifting is important for air pollution dispersion and weather prediction, but the small dimensions of mountain peaks often prevent numerical weather models from producing detailed forecasts. Mechanical lifting in stratified flow over mountains and associated thermodynamic processes were quantified and evaluated using Sheppard’s model to estimate the dividing-streamline height zt. The model was based on numerical weather model profile data and was evaluated using ground-based measurements on a tall, axisymmetric mountaintop for which the nondimensional mountain height hND = hN/U∞ is frequently between 1 and 10 (here h is mountain height, N is Brunt–Väisälä frequency, and U∞ is upstream horizontal wind speed). Sheppard’s formula was successful in predicting water vapor saturation at the mountaintop, with a false-prediction rate of 14.5%. Wind speed was found to be strongly related to the likelihood of forecast errors, and wind direction, season, and stratification did not play significant roles. The potential temperature (water vapor mixing ratio) at zt in the sounding was found to be slightly smaller (larger) than at the mountaintop, on average, indicating less lifting than predicted and/or turbulent mixing with higher-altitude air during parcel ascent. Detailed analysis revealed that this difference is a result of less lifting than predicted for small U∞/(Nh), whereas Sheppard’s model predicts the relative increase in uplift with increasing U∞/(Nh) correctly for U∞/(Nh) > 0.2.

scholarly journals On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

2013 ◽  
Vol 54 (62) ◽  
pp. 87-96 ◽  
Author(s):  
Marko Mäkynen ◽  
Bin Cheng ◽  
Markku Similä
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Barents Sea ◽  
Weather Prediction ◽  
Weather Conditions ◽  
The Arctic ◽  
Ice Thickness ◽  
Limited Area ◽  
Night Time ◽  
Modis Data

AbstractWe have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

scholarly journals A Fetch-Based Statistical Method to Bias Correct and Downscale Wind Speed over Unresolved Water Bodies

2017 ◽  
Vol 32 (4) ◽  
pp. 1637-1657 ◽  
Author(s):  
Bryan P. Holman ◽  
Steven M. Lazarus ◽  
Michael E. Splitt
Keyword(s):  
Wind Speed ◽  
Statistical Method ◽  
Bias Correction ◽  
Weather Prediction ◽  
Absolute Error ◽  
Water Bodies ◽  
Bias Error ◽  
Forecast Errors ◽  
Wind Fields ◽  
Water Characteristics

Abstract This paper presents a method to bias correct and downscale wind speed over water bodies that are unresolved by numerical weather prediction (NWP) models and analyses. The dependency of wind speeds over water bodies to fetch length is investigated as a predictor of model wind speed error. Because model bias is found to be related to the forecast wind direction, a statistical method that uses the forecast fetch to remove wind speed bias is developed and tested. The method estimates wind speed bias using recent forecast errors from similar stations (i.e., those with comparable fetch lengths). As a result, the bias correction is not tied to local observations but instead to locations with similar land–water characteristics. Thus, it can also be used to downscale wind fields over inland and coastal water bodies. The fetch method is compared to four reference bias correction methods using one year’s worth of wind speed output from three NWP analyses in Florida. The fetch method yields a bias error near zero and results in a reduction of the mean absolute error that is comparable to the reference methods. The fetch method is then used to bias correct and downscale a coarse analysis to 500-m grid spacing over a coastal estuary in central Florida.

scholarly journals Patterns of Land Surface Errors and Biases in the Global Forecast System

2011 ◽  
Vol 139 (5) ◽  
pp. 1569-1582 ◽  
Author(s):  
David Werth ◽  
Alfred Garrett
Keyword(s):  
Wind Speed ◽  
Sea Level ◽  
Air Temperature ◽  
Large Contribution ◽  
Global Forecast System ◽  
Forecast System ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Dewpoint Temperature ◽  
Mean Square Errors

One year’s worth of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, sea level pressure) are validated for land-based stations over the entire planet for forecasts extending from 0 h into the future (an analysis) to 7 days. Approximately 12 000 surface stations worldwide were included in this analysis. Root-mean-square errors (RMSEs) increased as the forecast period increased from 0 to 36 h, but the initial RMSEs were almost as large as the 36-h forecast RMSEs for all variables. Typical RMSEs were 3°C for air temperature, 2–3 mb for sea level pressure, 3.5°C for dewpoint temperature, and 2.5 m s−1 for wind speed. An analysis of the biases at each station shows that the biggest errors are associated with mountain ranges and other areas of steep topography, with land–sea contrasts also playing a role. When the error is decomposed into the bias, variance, and correlation terms, the large initial RMSEs for the 0-h forecasts are seen to be due to a large forecast bias (which persisted into the longer forecasts) with errors in forecast correlation also making a large contribution. A validation of two subdomains showed results similar to the global validation, but the dependence of the biases on the forecast time was clearer. Finally, the RMSE values climb as forecasts go out when validated out to a period of 7 days as the correlation error term grows.

scholarly journals Statistical Forecast of Daily Maximum Air Temperature in Arid Areas at Summertime

2020 ◽  
Vol 52 (3) ◽  
pp. 353-365
Author(s):  
Monim Al-Jiboori ◽  
Mahmoud Jawad Abu Al-Shaeer ◽  
Ahemd S. Hassan
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Absolute Error ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum ◽  
Forecast Errors ◽  
Arid Areas ◽  
Storm Events ◽  
Maximum Air Temperature

Based on historical observations of summers for the period from 2004 to 2018 with a focus on daily maximum and minimum air temperatures and wind speed recorded at 0600 GMT, a non-linear regression hypothesis is developed for forecasting daily maximum air temperature (Tmax) in arid areas such as Baghdad International airport station, which has a hot climate with no cloud cover or rain. Observations with dust storm events were excluded, thus this hypothesis could be used to predict daily Tmax on any day during summers characterized by fair weather. Using mean annual daily temperature range, daily minimum temperature, and the trend of maximum temperature with wind speed, Tmax was forecasted and then compared to those recorded by meteorological instruments. To improve the accuracy of the hypothesis, daily forecast errors, bias, and mean absolute error were analyzed to detect their characteristics through calculating relative frequencies of occurrence. At the end of this analysis, a value of (-0.45ºC) was added to the hypothesis as a bias term.

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