How well does the European Centre for Medium-Range Weather Forecasting Interim Reanalysis represent the surface air temperature in Cuban weather stations?

2017 ◽  
Vol 38 (3) ◽  
pp. 1216-1233 ◽  
Author(s):  
Albeht Rodríguez-Vega ◽  
Juan C. Antuña-Marrero ◽  
Michel D. S. Mesquita ◽  
Alan Robock ◽  
Thomas Toniazzo ◽  
...  
Keyword(s):  
Air Temperature ◽  
Weather Forecasting ◽  
Surface Air Temperature ◽  
European Centre ◽  
Medium Range ◽  
Weather Stations

scholarly journals Ship-Mounted Real-Time Surface Observational System on board Indian Vessels for Validation and Refinement of Model Forcing Fields*

2013 ◽  
Vol 30 (3) ◽  
pp. 626-637 ◽  
Author(s):  
R. Harikumar ◽  
T. M. Balakrishnan Nair ◽  
G. S. Bhat ◽  
Shailesh Nayak ◽  
Venkat Shesu Reddem ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Southern Ocean ◽  
Real Time ◽  
Weather Forecasting ◽  
Ocean Model ◽  
Weather Forecasts ◽  
European Centre ◽  
National Centre ◽  
Medium Range ◽  
Weather Stations

Abstract A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites [Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological–ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.

Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018

2021 ◽  
Vol 36 (1) ◽  
pp. 39-51
Author(s):  
Shoupeng Zhu ◽  
Xiefei Zhi ◽  
Fei Ge ◽  
Yi Fan ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Lead Time ◽  
Weather Forecasting ◽  
Heat Waves ◽  
Northeast Asia ◽  
Surface Air Temperature ◽  
Poor Quality ◽  
Spatiotemporal Patterns ◽  
Lead Times ◽  
Ensemble Mean

AbstractBridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluates products derived from four operational centers of CMA, KMA, NCEP, and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP), and Kalman filter superensemble (KF), in forecasts of surface air temperature with lead times of 6–30 days over northeast Asia in 2018. Validations after the preprocessing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The nonequal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after a lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2°–0.5°C relative to EMN. Forecast experiments of the 2018 northeast Asia heat wave reveal that the superensembles remarkably improve the raw forecasts featuring biases of >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at a lead time of 22 days. The superensemble techniques, particularly the KF method of dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal time scale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

scholarly journals An inkling of the relation between the monofractality of temperatures and pressure anomalies

2015 ◽  
Vol 2 (4) ◽  
pp. 1339-1353
Author(s):  
A. Deliège ◽  
S. Nicolay
Keyword(s):  
Wavelet Transform ◽  
Standard Deviation ◽  
Discrete Wavelet Transform ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Discrete Wavelet ◽  
Long Range Correlations ◽  
Hölder Exponents ◽  
Weather Stations

Abstract. We use the discrete "wavelet transform microscope" to study the monofractal nature of surface air temperature signals of weather stations spread across Europe. This method reveals that the information obtained in this way is richer than previous works studying long range correlations in meteorological stations: the approach presented here allows to bind the Hölder exponents with the standard deviation of surface pressure anomalies, while such a link does not appear with methods previously carried out.

A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends

2011 ◽  
Vol 24 (13) ◽  
pp. 3179-3189 ◽  
Author(s):  
Yuyu Ren ◽  
Guoyu Ren
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Image Data ◽  
Temperature Series ◽  
Urbanization Effect ◽  
Temperature Reference ◽  
Weather Stations ◽  
Reference Climate

Abstract In the global lands, the bias of urbanization effects still exits in the surface air temperature series of many city weather stations to a certain extent. Reliable reference climate stations need to be selected for the detection and correction of the local manmade warming bias. The underlying image data of remote sensing retrieval is adopted in this study to obtain the spatial distribution of surface brightness temperature, and the surface air temperature reference stations are determined based on the locations of the weather stations in the remote sensing surface thermal fields. Among the 672 national reference climate stations and national basic weather stations of mainland China, for instance, 113 surface air temperature reference stations are selected for applying this method. Compared with the average surface air temperature series of the reference stations obtained by a more sophisticated method developed in China, this method is proven to be robust and applicable, and can be adopted for the evaluation and adjustment study on the urbanization bias of the currently used air temperature records of surface climate stations in the global lands.

scholarly journals Estimating urban heat island effects on near-surface air temperature records of Uccle (Brussels, Belgium): an observational and modeling study

2011 ◽  
Vol 6 (1) ◽  
pp. 27-34 ◽  
Author(s):  
R. Hamdi ◽  
H. Van de Vyver
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Heat Island ◽  
Urban Bias ◽  
Near Surface ◽  
Urban Warming ◽  
Urban Heat ◽  
Weather Stations

Abstract. In this letter, the Brussels's urban heat island (UHI) effect on the near-surface air temperature time series of Uccle (the national suburban recording station of the Royal Meteorological Institute of Belgium) was estimated between 1955 and 2006 during the summer months. The UHI of Brussels was estimated using both ground-based weather stations and remote sensing imagery combined with a land surface scheme that includes a state-of-the-art urban parameterization, the Town Energy Balance scheme. Analysis of urban warming based on the remote sensing method reveals that the urban bias on minimum air temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The summer-mean urban bias on the mean air temperature is 0.8 °C (0.9 °C ground-based observed). The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of an ground-observational approach.

scholarly journals Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 81-91
Author(s):  
Amit Bhardwaj ◽  
Vinay Kumar ◽  
Anjali Sharma ◽  
Tushar Sinha ◽  
Surendra Pratap Singh
Keyword(s):  
Real Time ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Daily Rainfall ◽  
Ensemble Prediction ◽  
Individual Member ◽  
Ensemble Prediction System ◽  
Consensus Forecasts ◽  
European Centre ◽  
Medium Range

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

scholarly journals Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

2020 ◽  
Vol 24 (21) ◽  
pp. 16453-16482 ◽  
Author(s):  
Pradeep Hewage ◽  
Ardhendu Behera ◽  
Marcello Trovati ◽  
Ella Pereira ◽  
Morteza Ghahremani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
State Of The Art ◽  
Series Data ◽  
Proposed Model ◽  
Medium Range ◽  
Weather Stations ◽  
Local Weather

Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.

scholarly journals Intercomparison of Surface Temperatures from AIRS, MERRA, and MERRA-2 with NOAA and GC-Net Weather Stations at Summit, Greenland

2018 ◽  
Vol 57 (5) ◽  
pp. 1231-1245 ◽  
Author(s):  
Thomas J. Hearty ◽  
Jae N. Lee ◽  
Dong L. Wu ◽  
Richard Cullather ◽  
John M. Blaisdell ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Cold Season ◽  
Near Surface ◽  
Surface Temperatures ◽  
Air Temperatures ◽  
Weather Stations ◽  
The Difference ◽  
Temperature Inversions

AbstractThe surface skin and air temperatures reported by the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU-A), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and MERRA-2 at Summit, Greenland, are compared with near-surface air temperatures measured at National Oceanic and Atmospheric Administration (NOAA) and Greenland Climate Network (GC-Net) weather stations. The AIRS/AMSU-A surface skin temperature (TS) is best correlated with the NOAA 2-m air temperature (T2M) but tends to be colder than the station measurements. The difference may be the result of the frequent near-surface temperature inversions in the region. The AIRS/AMSU-A surface air temperature (SAT) is also correlated with the NOAA T2M but has a warm bias during the cold season and a larger standard error than the surface temperature. The extrapolation of the temperature profile to calculate the AIRS SAT may not be valid for the strongest inversions. The GC-Net temperature sensors are not held at fixed heights throughout the year; however, they are typically closer to the surface than the NOAA station sensors. Comparing the lapse rates at the two stations shows that it is larger closer to the surface. The difference between the AIRS/AMSU-A SAT and TS is sensitive to near-surface inversions and tends to measure stronger inversions than both stations. The AIRS/AMSU-A may be sampling a thicker layer than either station. The MERRA-2 surface and near-surface temperatures show improvements over MERRA but little sensitivity to near-surface temperature inversions.

Sign in / Sign up

Export Citation Format

Share Document