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 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.

Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning

“…Overall, using the developed approach, the evaluation metrics obtained in the rain area detection module were excellent in comparison with previous satellite-based [47][48][49][50][51][52]54,55]. Regarding the effectiveness of the model optimization, the results directly proved the practicability and feasibility of the rain area delineation algorithm in forecasting summer precipitation the next month and the next year.…”

“…During the process of model training, due to the significant imbalance of the two classes in the dataset that lowered the number of rainy pixels, directly randomly selecting the training dataset led to the underestimation of precipitation. To solve this problem, the sampling technique has been introduced by several researchers [51][52][53]55]. Notably, reducing the number of nonprecipitation pixels and then increasing the amount of precipitation pixels can alleviate the effects of imbalance.…”

“…Machine learning techniques provide new directions for the simulation of highly nonlinear relations to retrieval rainfall, such as neural networks [40][41][42][43][44][45][46], support vector machines [47], and gradient boosting decision trees [48]. The Random Forest (RF) algorithm also has been concerned in rainfall retrieval [49][50][51][52][53][54][55]. As an ensemble learning algorithm, random forest can generate reasonable predictions from a plurality of inputs by establishing multiple decision trees, and it is not easy to overfit compared to others and is usually superior to a single decision tree algorithm [56].…”

“…In summary, promising results were obtained in the previous study through the training dataset and validation set for the same period, while validation datasets for different periods have not been tested yet, resulting in an insufficient ability to convincingly prove the model. A significant issue was shown in that the intensity samples predicted by regression were concentrated below 0.5 mm/h, and the higher rate was underestimated by 3 mm/h in GPM [49,51,52,54,55]. To solve the above problems as much as possibly, the test dataset was extracted from an independent period, which differed from both the training and validation dataset with no overlap.…”

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

“…Early satellite-based rainfall retrieval efforts estimated rainfall from geostationary infrared (IR) data, using the indirect relationship between precipitation rate and the temperature of cloud on top [3]. Hence, the algorithms and the product accuracy were limited to the top of the cloud's characteristics.…”