Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events
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Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events
  • Published Date:

    2020

  • Source:
    Mon. Wea. Rev. (2020) 148 (3): 1005–1028.
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Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events
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  • Description:
    The recent successful deployment of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite R series (GOES-16/17) provides nearly uniform spatiotemporal measurements of total lightning (intracloud plus cloud to ground) over the Americas and adjacent vast oceanic regions. This study evaluates the potential value of assimilating GLM-derived water vapor mixing ratio on short-term (≤6 h), cloud-scale (dx = 1.5 km) forecasts of five severe weather events over the Great Plains of the United States using a three-dimensional variational (3DVAR) data assimilation (DA) system. Toward a more systematic assimilation of real GLM data, this study conducted sensitivity tests aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency, and the time window size for accumulating GLM lightning observations prior to the DA. Forecast statistics aggregated over all five cases suggested that an optimal forecast performance is obtained when lightning measurements are accumulated over a 10-min interval and GLM-derived water vapor mixing ratio values are assimilated every 15 min with a horizontal decorrelation length scale of 3 km. This suggested configuration for the GLM DA together with companion experiments (i) not assimilating any data, (ii) assimilating radar data only, and (iii) assimilating both GLM and radar data were evaluated for the same five cases. Overall, GLM data have shown potential to help improve the short-term (<3 h) forecast skill of composite reflectivity fields and individual storm tracks. While this result also held for accumulated rainfall, longer-term (≥3 h) forecasts were generally characterized by noteworthy wet biases.
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