An evaluation of NCEP Eta model predictions of surface energy budget and cloud properties by comparison with measured ARM data

Hinkelman, Laura M.; Ackerman, Thomas P.; Marchand, Roger

doi:10.1029/1999jd900120

Cited by 39 publications

(33 citation statements)

References 23 publications

(3 reference statements)

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“…Hinkelman et al (1999) find that the Eta Model has more inaccurately predicted clouds than correctly predicted clouds (based primarily on cloud height), as evaluated with observations at the Atmospheric Radiation Measurement (ARM) site in Oklahoma. In the Arctic, the greatest disagreement between eight different regional climate models was found in the predicted cloud cover, which led to errors in the surface radiation fluxes and 2-m temperature (Rinke et al 2006) as evaluated with data from the SHEBA project.…”

Section: Discussionmentioning

confidence: 95%

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Fogt

Bromwich

2008

Weather and Forecasting

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Antarctic Mesoscale Prediction System (AMPS) forecasts of atmospheric moisture and cloud fraction (CF) are compared with observations at McMurdo and Amundsen-Scott South Pole station (hereafter, South Pole station) in Antarctica. Overall, it is found that the model produces excessive moisture at both sites in the mid-to upper troposphere because of a weaker vertical decrease of moisture in AMPS than observed. Correlations with observations suggest AMPS does a reasonable job of capturing the low-level moisture variability at McMurdo and the upper-level moisture variability at South Pole station. The model underpredicts the cloud cover at both locations, but changes to the AMPS empirical CF algorithm remove this negative bias by more than doubling the weight given to the cloud ice path.A "pseudosatellite" product based on the microphysical quantities of cloud ice and cloud liquid water within AMPS is preliminarily evaluated against Defense Meteorological Satellite Program (DMSP) imagery during summer to examine the broader performance of cloud variability in AMPS. These comparisons reveal that the model predicts high-level cloud cover and movement with fidelity, which explains the good agreement between the modified CF algorithm and the observed CF. However, this product also demonstrates deficiencies in capturing low-level cloudiness over cold ice surfaces primarily related to insufficient supercooled liquid water produced by the microphysics scheme, which also reduces the CF correlation with observations.The results suggest that AMPS predicts the overall CF amount and high cloud variability notably well, making it a reliable tool for longer-term climate studies of these fields in Antarctica.

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Section: Discussionmentioning

confidence: 95%

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Fogt

Bromwich

2008

Weather and Forecasting

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“…These observations have been used to evaluate a number of numerical weather prediction (NWP) models (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001;Morcrette, 2002;Guichard et al, 2003;Sengupta et al, 2004;Yang et al, 2006;Illingworth et al, 2007;Hogan et al, 2009;Bouniol et al, 2010;Paquin-Ricard et al, 2010). In the work presented here, we sample four months and compare short-range forecasts with ground-based observations of liquid and ice cloud fraction at five locations.…”

Section: Introductionmentioning

confidence: 99%

“…Hogan et al, 2001;Yang et al, 2006;Illingworth et al, 2007), cloud frequency of occurrence (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001Hogan et al, , 2009Bouniol et al, 2010), cloud fraction histograms (e.g. Hogan et al, 2001;Illingworth et al, 2007;Bouniol et al, 2010;PaquinRicard et al, 2010) and contingency tables and skill scores (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two cloud parametrization schemes using ARM and Cloud‐Net observations

Morcrette

O’Connor

Petch

2011

Quart J Royal Meteoro Soc

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† The contribution of these authors was written in the course of their employment at the Met Office, UK, and is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.Ground-based remote-sensing observations from Atmospheric Radiation Measurement (ARM) and Cloud-Net sites are used to evaluate the clouds predicted by a weather forecasting and climate model. By evaluating the cloud predictions using separate measures for the errors in frequency of occurrence, amount when present, and timing, we provide a detailed assessment of the model performance, which is relevant to weather and climate time-scales. Importantly, this methodology will be of great use when attempting to develop a cloud parametrization scheme, as it provides a clearer picture of the current deficiencies in the predicted clouds.Using the Met Office Unified Model, it is shown that when cloud fractions produced by a diagnostic and a prognostic cloud scheme are compared, the prognostic cloud scheme shows improvements to the biases in frequency of occurrence of low, medium and high cloud and to the frequency distributions of cloud amount when cloud is present. The mean cloud profiles are generally improved, although it is shown that in some cases the diagnostic scheme produced misleadingly good mean profiles as a result of compensating errors in frequency of occurrence and amount when present. Some biases remain when using the prognostic scheme, notably the underprediction of mean ice cloud fraction due to the amount when present being too low, and the overprediction of mean liquid cloud fraction due to the frequency of occurrence being too high.

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“…Naúltima década, as RNAs tem tido uma crescente aplicação na estimativa e previsão de disponibilidade de recursos energéticos renováveis [11,[39][40][41]. Apesar de tratar-se de uma ferramenta estatística que considera relações empíricas entre as variáveis meteorológicas, as RNAs tem-se apresentado como um método alternativo com boa destreza frente a problemas não-lineares ou de natureza estocástica como os complexos processos físicos que influenciam na disponibilidade de energia solar e eólica [14].…”

Section: Aplicações Das Redes Neurais Artificiais Na Estimativa De Reunclassified

“…No entanto, as previsões fornecidas por esses modelos, para um ou dois dias de antecedência, apresentam desvios elevados em relação a valores medidos em superfície [8]. Estudos anteriores apontam como causa principal desses desvios elevados a dependência da irradiação solar em relaçãoà cobertura de nuvens eàs condições meteorológicas que envolvem intrinsecamente processos físicos não-lineares de difícil parametrização [11][12][13].…”

Section: Introductionunclassified

Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

Fiorin

Martins

Schuch

et al. 2011

Rev. Bras. Ensino Fís.

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Este trabalho tem como objetivo discutir de forma sucinta a ferramenta matemática conhecida como redes neurais artificiais e algumas aplicações naárea de energias renováveis. Inicialmente, o trabalho descreve a relevância desta ferramenta estatística nas diversasáreas do conhecimento e, posteriormente, conceitua e descreve as principais configurações possíveis de uma rede neural artificial. Por fim, o trabalho demonstra a aplicação da ferramenta para o levantamento de disponibilidade de recursos de energia solar no Brasil a partir de dados de superfície coletados em estações da rede SONDA operada pelo Centro de Ciência do Sistema Terrestre do Instituto Nacional de Pesquisas Espaciais. Os resultados obtidos até o momento mostram que as redes neurais podem fornecer estimativas com a confiabilidade necessária para avaliação da disponibilidade de energia solar e com melhor desempenho que outras técnicas estatísticas utilizadas na literatura. Palavras-chave: energia solar, redes neurais artificiais, modelagem atmosférica, modelos de mesoescala.This work aims at discussing the artificial neural networks (ANN) and some applications in renewable energy assessment. First, the paper describes the statistical relevance of this tool in different areas of knowledge and the main ANN concepts and configurations. Finally, the paper presents and discusses the use of ANN for the solar energy assessment in Brazil by using data collected in SONDA sites operated by the Center for Earth System Science of the Brazilian Institute for Space Research. The results show that ANN can provide reliable estimates with better performance than other statistical tools.

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An evaluation of NCEP Eta model predictions of surface energy budget and cloud properties by comparison with measured ARM data

Abstract: Abstract.Time

Cited by 39 publications

References 23 publications

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Evaluation of two cloud parametrization schemes using ARM and Cloud‐Net observations

Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

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