Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests

@article{Khnlein2014PrecipitationEF,
  title={Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests},
  author={Meike K{\"u}hnlein and Tim Appelhans and Boris Thies and Thomas Nauss},
  journal={Journal of Applied Meteorology and Climatology},
  year={2014},
  volume={53},
  pages={2457-2480}
}
AbstractA new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm… 
View via Publisher
journals.ametsoc.org
59 Citations
Highly Influential Citations
6
Background Citations
19
Methods Citations
16
Results Citations
5

59 Citations

Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit - Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D

A feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit, and a machine learning approach (Random Forest, RF) and a clear strategy to improve the IMERG product in the absence of MW radiances is presented.

High-resolution typhoon precipitation integrations using satellite infrared observations and multisource data

Abstract. Typhoon-related precipitation over land can result in severe disasters such as floods and landslides, and satellites are a valuable tool for estimating surface precipitation with high…

Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran

The validation results show a promising performance of the new rainfall retrieval technique, especially when compared to the GPM IMERG IR-only rainfall product, and uncertainties for the Lut Desert area and regions with high altitude gradients.

Supplementary material to "Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements"

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited…

Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data

ABSTRACT A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated…

Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning

  • Min MinChen Bai Jun Li
  • Environmental Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2019
Random forests, an advanced machine learning method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8, which produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI 9

This study is performed to retrieve precipitation amount using Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from Meteosat Second Generation (MSG). According to the relationship between the…

Satellite-based high-resolution mapping of rainfall over southern Africa

Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study…

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public…

Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms

...

References

SHOWING 1-10 OF 69 REFERENCES

Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI

SEVIRI rainfall retrieval and validation using weather radar observations

[1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared…

Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data

A new method for the delineation of precipitation during daytime using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective…

Rainfall-Rate Assignment Using MSG SEVIRI Data—A Promising Approach to Spaceborne Rainfall-Rate Retrieval for Midlatitudes

Abstract The potential of rainfall-rate assignment using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Instrument (SEVIRI) data is investigated. For this purpose, a new…

Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data

We propose a new method for the delineation of precipitation using cloud properties derived from optical satellite data. This approach is not only sufficient for the detection of mainly convective…

Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data

The present study aimed to investigate the potential of possible rain area delineation schemes based on the enhanced infrared spectral resolution of the Meteosat Second Generation–Spinning Enhanced…

Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the Meteosat Second…

GOES Multispectral Rainfall Algorithm (GMSRA)

Abstract A multispectral approach is used to optimize the identification of raining clouds located at a given altitude estimated from the cloud-top temperature. The approach combines information from…

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

For over two decades operational rainfall estimations from geostationary satellites have represented an ambitious aspiration of scientists and an identified need of operational meteorologists. A wide…

Investigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical models

Summary The present paper describes two model-coupled approaches for the determination of rain rates from remote sensing data. The idea of both approaches for retrieving precipitation is to account…
...