Abstract
This study evaluates the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project precipitation products over Papua New Guinea (PNG). The products evaluated were based on remotely-sensed precipitation, vegetation health, soil moisture, and outgoing longwave radiation (OLR) data. The satellite precipitation estimates of the Climate Prediction Center/National Oceanic and Atmospheric Administration’s (CPC/NOAA) morphing technique (CMORPH) and Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) were assessed on a monthly timescale over an 18-year period from 2001 to 2018. Station data along with the ERA5 reanalysis were used as the reference datasets for assessment purposes. In addition, a case study was performed to investigate how well the SWCEM precipitation products characterised drought in PNG associated with the 2015–2016 El Niño. Overall statistics from the validation study suggest that although there remains significant variability between satellite and ERA5 rainfall data in remote areas, this difference is much less at locations where rain gauges exist. The case study illustrated that the Vegetation Health Index (VHI), OLR anomaly and the Standardized Precipitation Index (SPI) were able to reliably capture the spatial and temporal aspects of the severe 2015–2016 El Niño-induced drought in PNG. Of the three, VHI appeared to be the most effective, in part due to its reduced incidence of false alarms. This study is novel as modern-day satellite-derived products have not been evaluated over PNG before. A focus on their value in monitoring drought can bring great value in mitigating the impact of future droughts. It is concluded that these satellite-derived precipitation products could be recommended for operational use for drought detection and monitoring in PNG, and that even a modest increase in ground-based observations will increase the accuracy of satellite-derived observations remotely.
Highlights
Droughts frequently occur in Asia-Pacific countries affecting many people and impacting on the well-being and economy of populations
Six satellite-derived variables were used in the case study—Global Satellite Mapping of Precipitation (GSMaP) rainfall and GSMaP standardized precipitation index (SPI) from Japan Aerospace Exploration Agency (JAXA); normalized difference vegetation index (NDVI), vegetation health index (VHI), and soil moisture and outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration (NOAA)
The high dependency of Papua New Guinea (PNG)’s population and economy on rainfall along with the country’s vulnerability to extreme rainfall events has motivated the generation of a suite of satellite-derived precipitation products for use in the country via the World Meteorological Organization (WMO)’s Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project. These products were validated in two parts: (i) a validation study of the precipitation estimates and (ii) a case study focusing on how well the SWCEM satellite-derived precipitation products represented the severe 2015–2016 drought in PNG
Summary
Droughts frequently occur in Asia-Pacific countries affecting many people and impacting on the well-being and economy of populations. In 2017, WMO initiated the Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project to assist countries in the Asia-Pacific region with operational utilisation of satellite precipitation estimates, with a particular focus on monitoring precipitation extremes—accumulated heavy rainfall and drought [8]. The SWCEM products include satellite-derived estimates of precipitation, soil moisture, and outgoing longwave radiation (OLR) estimates as well as processed products such as the Standardized Precipitation Index (SPI) and vegetation health indices The validation of these products over PNG is a critical step in determining their value in providing operational rainfall monitoring and early drought detection, and is addressed in this study. To verify the accuracy of satellite precipitation estimates over PNG, this study employs a similar validation methodology used in our earlier research that assessed the accuracy of CMORPH and GSMaP datasets over Australia [15]
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