How Much Can AI Techniques Improve Surface Air Temperature Forecast? —A Report from AI Challenger 2018 Global Weather Forecast Contest

Aerosol effects on the forecast of surface temperature, as well as temperature at the levels of 850 and 500 hPa over Europe and the European part of Russia are studied using various aerosol climatologies: Tanre, Tegen, and MACv2. The numerical experiments with the COSMO-Ru model are performed for the central months of the seasons (January, April, July, and October) in 2017. It is found that a change in the simulated surface air temperature over land can reach 1C when using Tegen and MACv2 data as compared to Tanre. At 850 and 500 hPa levels, the changes do not exceed 0.4C. At the same time, it is shown that a decrease in the root-mean-square error of 2-m air temperature forecast at individual stations reaches 0.5C when using Tegen and MACv2 data and 1C for clear-sky conditions in Moscow.

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<i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

Nonlinear Processes in Geophysics ◽

10.5194/npg-17-269-2010 ◽

2010 ◽

Vol 17 (3) ◽

pp. 269-272 ◽

Cited By ~ 5

Author(s):

S. Nicolay ◽

G. Mabille ◽

X. Fettweis ◽

M. Erpicum

Keyword(s):

Air Temperature ◽

Climate Model ◽

Surface Air Temperature ◽

Weather Forecast ◽

Temperature Data ◽

Temperature Time Series ◽

Climatic Data ◽

Statistical Validation ◽

Confidence Levels ◽

Temperature Time

Abstract. Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

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Can temperatures from an online weather forecast service be suitable for modelling growth stages using a CERES-Wheat type phenology model?

Advances in Animal Biosciences ◽

10.1017/s2040470017000383 ◽

2017 ◽

Vol 8 (2) ◽

pp. 684-688

Author(s):

M. Launspach ◽

J.A. Taylor ◽

J. Wilson

Keyword(s):

Air Temperature ◽

Plant Development ◽

Weather Forecast ◽

Growth Stages ◽

North East ◽

Temperature Forecast ◽

Temperature And Precipitation ◽

Station Data ◽

Forecast Data ◽

Phenology Model

Weather and climate have a fundamental impact on plant development. Monitoring key observables, e.g. temperature and precipitation, is paramount for the interpretation of agricultural experiments and simulation of plant development. Whereas the presence of appropriate sensors in a research environment can be expected, the situation can be different in commercial agricultural settings. Local air temperature from online weather forecasts is investigated as a substitute for local weather station data. Hourly air temperature forecast and station data for several locations in Scotland and North East England are aggregated into daily air temperature values spanning a period of several months. Dates for key growth stages using temperatures from weather stations and weather forecast data are compared. For the examples discussed here the date differences in modelled key growth stages did not exceed 3 days indicating that temperature forecast data is suitable for farm-specific applications.

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Utilization of Self―Organizing Maps to analyzing systematic bias in numerical model : Surface Air Temperature forecast of KMA GDAPS

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2018.28.6.601 ◽

2018 ◽

Vol 28 (6) ◽

pp. 601-610

Author(s):

Sanghee Jun ◽

Jeong―Hyun Park ◽

Hee―Jeong Baek ◽

Kyung―On Boo ◽

Hyun―Suk Kang

Keyword(s):

Numerical Model ◽

Air Temperature ◽

Surface Air Temperature ◽

Systematic Bias ◽

Model Surface ◽

Self Organizing Maps ◽

Temperature Forecast ◽

Self Organizing

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Prediction Skill of NCEP CFSv2 for Seasonal Precipitation and Surface Air Temperature Forecast over Southeast Asia

Sains Malaysiana ◽

10.17576/jsm-2019-4811-04 ◽

2019 ◽

Vol 48 (11) ◽

pp. 2325-2334

Author(s):

Siti Amalia Siti Amalia ◽

Fredolin Tangang ◽

Tieh Ngai Sheau ◽

Juneng Liew

Keyword(s):

Southeast Asia ◽

Air Temperature ◽

Surface Air Temperature ◽

Prediction Skill ◽

Seasonal Precipitation ◽

Temperature Forecast

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Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0022.1 ◽

2021 ◽

Author(s):

Randal D. Koster ◽

Anthony M. DeAngelis ◽

Siegfried D. Schubert ◽

Andrea M. Molod

Keyword(s):

Soil Moisture ◽

Air Temperature ◽

Forecast Error ◽

Surface Air Temperature ◽

Forecast Bias ◽

Temperature Forecast ◽

Independent Observations ◽

Moisture State ◽

The Relationship ◽

Soil Moisture Initialization

AbstractSoil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

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