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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Large-Scale Influences on Atmospheric River Induced Extreme Precipitation Events Along the Coast of Washington State

Transient, narrow plumes of strong water vapor transport, referred to as AtmosphericRivers (ARs), are responsible for much of the precipitation along the west coast of the UnitedStates. The most intense precipitation events are almost always induced by an AR on the coast ofOregon and Washington and can result in detrimental impacts on society due to mudslides andflooding. In order to accurately predict AR events on numerical weather prediction, subseasonal,and seasonal timescales, it is important to understand the large-scale impacts on extreme ARevents. Here, characteristics of ARs that result in an extreme precipitation event are compared totypical ARs on the coast of Washington State. In addition to more intense water vapor transport,notable differences in the synoptic forcing are present during extreme precipitation events thatare not present during typical AR events. Subseasonal and seasonal teleconnection patterns areknown to influence the weather in the Pacific Northwest and are investigated here. The MaddenJulian Oscillation (MJO) plays a role in determining the strength of precipitation associated withan AR on the Washington Coast. Phase 5 of the MJO (convection centered over the maritimecontinent) is the most common phase during an extreme precipitation event, while phase 2(convection over the Indian Ocean) discourages an extreme event from occurring. Interactionsbetween El Niño Southern Oscillation (ENSO) and the propagation speed of the MJO result inextreme events during phase 1 of the MJO and El Niño but phase 8 during neutral ESNOconditions.

Allison B Marquardt Collow↗
NASA NTRS2021

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↗