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Abstract. Assessments of water balance changes, watershed response, and landscapeevolution to climate change require representation of spatially andtemporally varying rainfall fields over a drainage basin, as well as theflexibility to simply modify key driving climate variables (evaporativedemand, overall wetness, storminess). An empirical–stochastic approach to theproblem of rainstorm simulation enables statistical realism and the creationof multiple ensembles that allow for statistical characterization and/or timeseries of the driving rainfall over a fine grid for any climate scenario.Here, we provide details on the STOchastic Rainfall Model (STORM), which usesthis approach to simulate drainage basin rainfall. STORM simulates individualstorms based on Monte Carlo selection from probability density functions(PDFs) of storm area, storm duration, storm intensity at the core, and stormcenter location. The model accounts for seasonality, orography, and theprobability of storm intensity for a given storm duration. STORM alsogenerates time series of potential evapotranspiration (PET), which arerequired for most physically based applications. We explain how the modelworks and demonstrate its ability to simulate observed historical rainfallcharacteristics for a small watershed in southeast Arizona. We explain the datarequirements for STORM and its flexibility for simulating rainfall forvarious classes of climate change. Finally, we discuss several potentialapplications of STORM.
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Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process
Abstract A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of storm intensity change and associates each state with a probability distribution of intensity change. The storm’s transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Markov Environment-Dependent Hurricane Intensity Model (MeHiM) is used to simulate the evolution of storm intensity along the storm track over the ocean, and a simple decay model is added to estimate the intensity change when the storm moves over land. Data for the North Atlantic (NA) basin from 1979 to 2014 (555 storms) are used for model development and evaluation. Probability distributions of 6- and 24-h intensity change, lifetime maximum intensity, and landfall intensity based on model simulations and observations compare well. Although the MeHiM is still limited in fully describing rapid intensification, it shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models).
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- Award ID(s):
- 1652448
- PAR ID:
- 10189379
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 32
- Issue:
- 22
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- 7837 to 7855
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JCLI-D-19-0027.1
