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Retrieval of Cloud Top Pressure
Linköpings universitet, Institutionen för datavetenskap. Linköpings universitet, Tekniska fakulteten.
2016 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

In this thesis the predictive models the multilayer perceptron and random forest are evaluated to predict cloud top pressure. The dataset used in this thesis contains brightness temperatures, reflectances and other useful variables to determine the cloud top pressure from the Advanced Very High Resolution Radiometer (AVHRR) instrument on the two satellites NOAA-17 and NOAA-18 during the time period 2006-2009. The dataset also contains numerical weather prediction (NWP) variables calculated using mathematical models. In the dataset there are also observed cloud top pressure and cloud top height estimates from the more accurate instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The predicted cloud top pressure is converted into an interpolated cloud top height. The predicted pressure and interpolated height are then evaluated against the more accurate and observed cloud top pressure and cloud top height from the instrument on the satellite CALIPSO.

The predictive models have been performed on the data using different sampling strategies to take into account the performance of individual cloud classes prevalent in the data. The multilayer perceptron is performed using both the original response cloud top pressure and a log transformed repsonse to avoid negative values as output which is prevalent when using the original response. Results show that overall the random forest model performs better than the multilayer perceptron in terms of root mean squared error and mean absolute error.

sted, utgiver, år, opplag, sider
2016. , s. 77
Emneord [en]
neural networks, multilayer perceptron, random forest regression, cloud top pressure, cloud top height
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-129805ISRN: LIU-IDA/STAT-A--16/006—SEOAI: oai:DiVA.org:liu-129805DiVA, id: diva2:944014
Eksternt samarbeid
Swedish Meteorological and Hydrological Institute (SMHI)
Fag / kurs
Statistics
Veileder
Examiner
Tilgjengelig fra: 2016-06-29 Laget: 2016-06-28 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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Retrieval of Cloud Top Pressure(639 kB)318 nedlastinger
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