Dataset Information

Identifying errors in dust models from data assimilation.

ABSTRACT: Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development.

SUBMITTER: Pope RJ

PROVIDER: S-EPMC5082526 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Identifying errors in dust models from data assimilation.

Pope R J RJ Marsham J H JH Knippertz P P Brooks M E ME Roberts A J AJ

Geophysical research letters 20160903 17

Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model ...[more]

PMID: 27840459

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Project description:Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

Dataset Information

Identifying errors in dust models from data assimilation.

Publications

Identifying errors in dust models from data assimilation.

Similar Datasets

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