7. Conclusion

System, (MHS) Microwave Humidity Sounder, (GPCP) Global Precipitation Climatology Centre, (GOES) Geostationary Operational Environmental Satellite, (Metsat) meteorological satellite, (NOAA) NASA-provided TIROS series of weather forecasting satellite run by the National Oceanic and Atmospheric Administration, (DMSP) Defense Meteorological Satellite Program, (GRIDSAT-B1) geostationary IR

The capacity to acquire information from remote sensing data has been improved to an unprecedented level, accumulating overwhelming amounts of information. For example, the Google Earth Engine (GEE) [105] is updated at a rate of nearly 6000 scenes per day from active missions (a typical image 10 km by 10 km requires 50–200 million bytes of memory). Such a large amount of data requires not only vast amounts of memory data but also higher-level services with high-performance computing systems [106]. Successful experiences have already been recorded [97], but the GEE is worth mentioning [105]. GGE stores a multipetabyte catalog of satellite imagery and geospatial data sets collected from different resources and provides high-performance computing systems that can be

channel brightness temperature.

Representative satellite rainfall products.

Product Spatial

Drought - Detection and Solutions

CHIRPSv2.0 Funk et al. [99]

PERSIANN Nguyen et al. [100]

PERSIANN-CDR Ashouri et al. [101]

CMORPH Joyce et al. [102]

RFE2.0 Xie and Arkin [103]

TRMM3B42 Huffman et al. [104]

Table 2.

18

coverage

0.05° 0.05° 50°S–50°N

0.25° 0.25° 60°S–60°N

0.25° 0.25° 60°S–60°N

0.25° 0.25° 60°S–60°N

0.1° 0.1° 40°N–40°S 20°W–55°E

0.25° 0.25° 50°N–50°S

Temporal coverage

Daily, pentadal and monthly 1981 to near present

1,3,6 hours March 2000 to present

> Daily, monthly 1983 to 2017

30 min 2002 to present

daily 2001 to present

3 hourly/ daily 1998 to 2015.

Inputs Access

IR (GOES-8, GOES-10, GMS-5, Metsat-6, and Metsat-7) corrected with MW(DMSP 7, 8, and 9 and NOAA-15, 16, and 17)

> IR (GRIDSAT-B1) +GPCP correction

SSM/I (DMSP 13, 14, and 15) AMSU-B (NOAA-15, 16, 17, and 18) AMSU-E (Aqua), TMI (TRMM) Geostationary satellite IR

> MW (SSM/I, AMSU-B) IR (GTS)

MW (TRMM, SSM/I, AMSR, AMSU), IR

IR + GG http://chg.geog.ucsb.

edu/data/

https://chrsdata.eng. uci.edu/

http://chrsdata.eng.uci. edu/

http://www.cpc.ncep. noaa.gov/products/ janowiak/ cmorph\_description. html

https://iridl.ldeo. columbia.edu/ SOURCES/.NOAA/. NCEP/.CPC/.FEWS/

https://pmm.nasa. gov/data-access/ downloads/trmm

6. Accessing and processing the data

Climate change is shifting global rainfall patterns and will increase the intensity and duration of drought around the world; this produces the need to take contingency actions to prevent the impact of famine. ML models, an evolving research area, are a valuable complement to methods previously proposed for forecasting drought. Results obtained so far for predicting meteorological indices are very satisfactory, especially with hybrid models such as WT-ANN or WT-SVR.

Most of the work that we reported here is based on the standardized precipitation index or SPI, which is a reliable measure of drought used in more than 60 countries. The leading month or the number of months over which SPI is calculated significantly influences the prediction values.

Unfortunately, many of the examples were based on ground gauge data. The brevity (and noise) of the records obstructs the use of many satellite products. However, as time progresses and data retrieval improves, satellite products will be long and accurate enough to generate reliable results.

The exponential growth of public and free satellite imagery sources and of opensource software, as well as cheaper access to cloud-based technology, will provide powerful forecasting tools to a greater number of researchers, allowing them to forecast drought before it occurs.

#### Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Drought - Detection and Solutions
