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

Kolbe, Christine; Thies, Boris; Egli, Sebastian; Lehnert, Lukas W.; Schulz, H. M.; Bendix, Jörg

doi:10.3390/rs11192302

Cited by 7 publications

(16 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the IR-only IMERG, the RF models showed distinct improvements in rain area delineation ( Figure 7) and rainfall rate assignment (Figure 8). This is in agreement with the results of Kolbe et al (2019Kolbe et al ( , 2020, and Turini et al (2019). Also, we compared our product with INAMHI gauge measurements.…”

Section: Discussionsupporting

confidence: 90%

“…The number of available models for one day ranges from 1 to 4 on average, depending on the time of year ( Figure 2). In regions with complex topography, the higher temporal resolution models lead to better precipitation estimates (Kolbe et al, 2019).…”

Section: The Architecture and Configuration Of The Rainfall Retrievalmentioning

confidence: 99%

“…Several studies have shown that multispectral satellite data provide information about microphysical and optical cloud properties (Inoue, 1985;Lensky & Rosenfeld, 2003;Ou et al, 1993;Stone et al, 1990;Strabala et al, 1994); therefore, such data can improve satellite-based rainfall retrievals (Kolbe et al, 2019(Kolbe et al, , 2020Kühnlein et al, 2014aKühnlein et al, , 2014bTurini et al, 2019). Thies et al (2008) demonstrated that the channel differences between IR bands provide information about the cloud phase, the cloud top, and the cloud water path, which can improve satellite-based rainfall retrievals (Kolbe et al, 2020;Kühnlein et al, 2014b;Turini et al, 2019).…”

Section: Goesmentioning

confidence: 99%

“…Spatial information, such as the variance within spectral bands, which are important for capturing clouds' heterogeneity, cannot be obtained by only looking at single pixel values (Egli et al, 2018). Information about the spatial variance within a given raster dataset has improves the output of satellitebased cloud detection and rainfall retrieval techniques (Egli et al, 2018;Kolbe et al, 2019Kolbe et al, , 2020Schulz et al, 2017;Turini et al, 2019). To consider the spatial variability of raining clouds, texture features were calculated from these predictor variables using a 5 × 5 pixel moving window approach.…”

Section: Goesmentioning

confidence: 99%

“…To consider the spatial variability of raining clouds, texture features were calculated from these predictor variables using a 5 × 5 pixel moving window approach. For individual predictors, variograms (VARs), madograms (MADs), and rodograms (RODs) were calculated, while for each possible predictor variable combination, cross-variograms (CVs) and pseudo cross-variograms (PCVs) were calculated (Egli et al, 2018;Kolbe et al, 2019Kolbe et al, , 2020Turini et al, 2019) (please refer to Schulz et al (2017) for more information). In addition to the GOES-16 IR channels, the BCM was used to restrict the rainfall retrieval algorithm to cloudy pixels.…”

Section: Goesmentioning

confidence: 99%

See 4 more Smart Citations

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

Turini

Thies

Horna³

et al. 2021

European Journal of Remote Sensing

Self Cite

View full text Add to dashboard Cite

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 Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes's high elevation. Comparison of RF rainfall rates in 2 km 2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: The Architecture and Configuration Of The Rainfall Retrievalmentioning

confidence: 99%

Section: Goesmentioning

confidence: 99%

Section: Goesmentioning

confidence: 99%

Section: Goesmentioning

confidence: 99%

See 3 more Smart Citations

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

Turini

Thies

Horna³

et al. 2021

European Journal of Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes

et al. 2021

Self Cite

View full text Add to dashboard Cite

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km2 and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km2 and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments.

show abstract

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

et al. 2022

View full text Add to dashboard Cite

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 services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the new-generation geostationary satellite, have been used as predictor variables and the Global Precipitation Measurement (GPM) precipitation product has been employed to train the retrieval model. We utilized two data normalization schemes, area-based and pixel-based normalization, and conducted comparative experiments. Comparing the estimated results with the GPM product on the test set, PRSOT_Pixel_based model achieved a Probability Of Detection (POD) of 0.74, a False Alarm Ratio (FAR) of 0.44 and a Critical Success Index (CSI) of 0.47 for two-class metrics, and an Accuracy (ACC) of 0.75 for multi-class metrics. Pixel-based normalization is more suitable for meteorological data, highlighting the precipitation characteristics and obtaining better comprehensive retrieval performance in visualization and evaluation metrics. In conclusion, the proposed PRSOT model has made a remarkable and essential contribution to precipitation retrieval and outperforms the benchmark machine learning model Random Forests.

show abstract

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

Cited by 7 publications

References 50 publications

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

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

Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

Contact Info

Product

Resources

About