IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i6p1232-d1171746.html
   My bibliography  Save this article

Evaluation of CAMEL over the Taklimakan Desert Using Field Observations

Author

Listed:
  • Yufen Ma

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Wei Han

    (China Meteorological Administration Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China
    The State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China)

  • Zhenglong Li

    (Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI 53706, USA)

  • E. Eva Borbas

    (Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI 53706, USA)

  • Ali Mamtimin

    (Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
    National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China
    Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China
    Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China)

  • Yongqiang Liu

    (College of Resource and Environmental Sciences, Xinjiang University, Urumqi 830049, China)

Abstract

Infrared (IR) land surface emissivity (LSE) plays an important role in numerical weather prediction (NWP) models through the satellite radiance assimilation. However, due to the large uncertainties in LSE over the desert, many land-surface sensitive channels of satellite IR sensors are not assimilated. This calls for further assessments of the quality of satellite-retrieved LSE in these desert regions. A set of LSE observations were made from field experiments conducted on 16–18 October 2013 along a south/north desert road in the Taklimakan Desert (TD), China. The observed LSEs (EOBS) are thus used in this study as the reference values to evaluate the quality of Combined ASTER MODIS Emissivity over Land (CAMEL) data. Analysis of these data shows four main results. First, the CAMEL datasets appear to sufficiently capture the spatial variations in LSE from the oasis to the hinterland of the TD (this is especially the case in the quartz reststrahlen band). From site 1 at the southern edge of the Taklimakan Desert to site 10 at the northern edge, the measured LSE and the corresponding CAMEL observation in the quartz reststrahlen band first decrease and reach their minimum around sites 4–6 in the hinterland of the Taklimakan Desert. Then, the LSE increases gradually and finally reaches its maximum at site 10, which has a clay ground surface, showing that the LSE is higher at the edges of the desert and lower in the center. Second, the CAMEL values at 11.3 μm have a zonal distribution characterized by a northeast–southwest strike, though such an artifact might have been introduced by ASTER LSE data during the merging process that created the CAMEL dataset. Third, the unrealistic variation of the original EOBS can be filtered out with useful signals, as identified by the first six principal components of the PCA conducted on the laboratory-measured hyperspectral emissivity spectra (ELAB). Fourth, the CAMEL results correlate well with the measured LSE at the 10 observation sites, with the observed LSE being slightly smaller than the CAMEL values in general.

Suggested Citation

  • Yufen Ma & Wei Han & Zhenglong Li & E. Eva Borbas & Ali Mamtimin & Yongqiang Liu, 2023. "Evaluation of CAMEL over the Taklimakan Desert Using Field Observations," Land, MDPI, vol. 12(6), pages 1-21, June.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1232-:d:1171746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/6/1232/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/6/1232/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michele Torresani & Guido Masiello & Nadia Vendrame & Giacomo Gerosa & Marco Falocchi & Enrico Tomelleri & Carmine Serio & Duccio Rocchini & Dino Zardi, 2022. "Correlation Analysis of Evapotranspiration, Emissivity Contrast and Water Deficit Indices: A Case Study in Four Eddy Covariance Sites in Italy with Different Environmental Habitats," Land, MDPI, vol. 11(11), pages 1-16, October.
    2. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Linsenmeier, Manuel & Shrader, Jeffrey G., 2023. "Global inequalities in weather forecasts," SocArXiv 7e2jf, Center for Open Science.
    2. Jinhua Wen & Yian Hua & Chenkai Cai & Shiwu Wang & Helong Wang & Xinyan Zhou & Jian Huang & Jianqun Wang, 2023. "Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    3. Mayer, Martin János & Yang, Dazhi, 2023. "Calibration of deterministic NWP forecasts and its impact on verification," International Journal of Forecasting, Elsevier, vol. 39(2), pages 981-991.
    4. Anand, Vaibhav, 2022. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," MPRA Paper 114491, University Library of Munich, Germany.
    5. Chuyuan Lin & Ying Yu & Lucas Y. Wu & Jiguo Cao, 2023. "Unsupervised learning on U.S. weather forecast performance," Computational Statistics, Springer, vol. 38(3), pages 1193-1213, September.
    6. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    7. Liu, Jiarui & Fu, Yuchen, 2023. "Renewable energy forecasting: A self-supervised learning-based transformer variant," Energy, Elsevier, vol. 284(C).
    8. Husain Najafi & Pallav Kumar Shrestha & Oldrich Rakovec & Heiko Apel & Sergiy Vorogushyn & Rohini Kumar & Stephan Thober & Bruno Merz & Luis Samaniego, 2024. "High-resolution impact-based early warning system for riverine flooding," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    10. Tang, Wenliang & Yang, Mian & Duan, Hongbo, 2023. "Temperature and corporate tax avoidance: Evidence from Chinese manufacturing firms," Energy Economics, Elsevier, vol. 117(C).
    11. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    12. Zack Guido & Sara Lopus & Kurt Waldman & Corrie Hannah & Andrew Zimmer & Natasha Krell & Chris Knudson & Lyndon Estes & Kelly Caylor & Tom Evans, 2021. "Perceived links between climate change and weather forecast accuracy: new barriers to tools for agricultural decision-making," Climatic Change, Springer, vol. 168(1), pages 1-20, September.
    13. Sergei Soldatenko & Rafael Yusupov, 2021. "An Optimal Control Perspective on Weather and Climate Modification," Mathematics, MDPI, vol. 9(4), pages 1-15, February.
    14. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
    15. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    16. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    17. Yang, Dazhi & Kleissl, Jan, 2023. "Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1640-1654.
    18. Alexander Henzi & Johanna F. Ziegel & Tilmann Gneiting, 2021. "Isotonic distributional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 963-993, November.
    19. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks," Mathematics, MDPI, vol. 12(3), pages 1-17, February.
    20. Karma Tsering & Manish Shrestha & Kiran Shakya & Birendra Bajracharya & Mir Matin & Jorge Luis Sanchez Lozano & Jim Nelson & Tandin Wangchuk & Binod Parajuli & Md Arifuzzaman Bhuyan, 2022. "Verification of two hydrological models for real-time flood forecasting in the Hindu Kush Himalaya (HKH) region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1821-1845, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1232-:d:1171746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.