Forecasts of Opportunity for Northern California Soil Moisture

Cécile Penland, Megan D. Fowler, Darren L. Jackson and Robert Cifelli
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Cécile Penland: NOAA ESRL/Physical Sciences Laboratory, Boulder, CO 80305, USA
Megan D. Fowler: NOAA ESRL/Physical Sciences Laboratory, Boulder, CO 80305, USA
Darren L. Jackson: NOAA ESRL/Physical Sciences Laboratory, Boulder, CO 80305, USA
Robert Cifelli: NOAA ESRL/Physical Sciences Laboratory, Boulder, CO 80305, USA

Land, 2021, vol. 10, issue 7, 1-24

Abstract: Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences, yet systematic studies of soil moisture predictability are limited. Here, we use a data-adaptive technique, Linear Inverse Modeling, which has proved useful as an indication of predictability in other fields, to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1–2 weeks. An important advantage of this model is the a priori identification of forecasts of opportunity—conditions under which the model’s forecasts may be expected to have particularly high skill. Given that forecast errors (and inversely, their skill) can be estimated in advance, these findings have the potential to greatly increase the utility of soil moisture forecasts for practical applications including drought and flood forecasting.

Keywords: soil moisture; predictability; forecasts of opportunity; linear inverse modeling; California hydrology (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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