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Developing a Hydrological Monitoring and Sub-Seasonal to Seasonal Forecasting System for South and Southeast Asian River Basins

South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Yifan Zhou↗

Tropical Tropospheric Ozone Trends (1990 to 2022): A Re-evaluation Based on SHADOZ and IAGOS Profiles and TOMS/OMI Columns

Changes in tropical tropospheric ozone (TTO) are of importance because this region spans roughly a third of the Earth and portions of it are experiencing variability in trends of ozone precursors (CO, NO x , CH 4 and nonmethane hydrocarbons) associated with economic growth and fires. In addition to ozone changes affecting radiative forcing, tropical ozone is an important source of the OH radical and thus, the oxidizing capacity of the planet (Thompson, 1992). Recent studies examining TTO trends satellite and in-situ observations over the past ~25 years include: Thompson et al., JGR, 2021; Gaudel et al., ACP, 2023; Stauffer et al., ACP, 2023. The results show considerable regional and seasonal variability in TTO trends and sensitivity to data selection, frequency, and statistical method used. The satellite data vary most widely in method, time period and reliability. Here we revisit trends for the 1990-2022 period with the best-characterized buv-based satellite products that span that period (derived from TOMS and OMI, Ziemke et al., 2019). The satellite-based trends are compared to trends based on in-situ data from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network (1998-2022), measurements from selected pre-SHADOZ and IAGOS commercial aircraft data (Gaudel et al., 2023). Among sensitivities examined are the dependence of ozone trends on start and end years, impacts of ENSO events and the COVID-19 perturbation to emissions. Two statistical methods are used, quantile regression (QR) and multiple linear regression (MLR). Trends of total TTO, ozone segments in the boundary layer (to ~700 hPa), and free troposphere (700-300 hPa) are compared.

ozone, OMI, tropospheric ozone, SHADOZ↗