Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: Alejandro Flores x
  • All content x
Clear All Modify Search
William Rudisill, Alejandro Flores, and James McNamara

Abstract

Snow’s thermal and radiative properties strongly impact the land surface energy balance and thus the atmosphere above it. Land surface snow information is poorly known in mountainous regions. Few studies have examined the impact of initial land surface snow conditions in high-resolution, convection-permitting numerical weather prediction models during the midlatitude cool season. The extent to which land surface snow influences atmospheric energy transport and subsequent surface meteorological states is tested using a high-resolution (1 km) configuration of the Weather Research and Forecasting (WRF) Model, for both calm conditions and weather characteristic of a warm late March atmospheric river. A set of synthetic but realistic snow states are used as initial conditions for the model runs and the resulting differences are compared. We find that the presence (absence) of snow decreases (increases) 2-m air temperatures by as much as 4 K during both periods, and that the atmosphere responds to snow perturbations through advection of moist static energy from neighboring regions. Snow mass and snow-covered area are both important variables that influence 2-m air temperature. Finally, the meteorological states produced from the WRF experiments are used to force an offline hydrologic model, demonstrating that snowmelt rates can increase/decrease by factor of 2 depending on the initial snow conditions used in the parent weather model. We propose that more realistic representations of land surface snow properties in mesoscale models may be a source of hydrometeorological predictability

Open access
Scott Havens, Danny Marks, Katelyn FitzGerald, Matt Masarik, Alejandro N. Flores, Patrick Kormos, and Andrew Hedrick

Abstract

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.

Full access
Liao-Fan Lin, Ardeshir M. Ebtehaj, Rafael L. Bras, Alejandro N. Flores, and Jingfeng Wang

Abstract

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

Full access
Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

Abstract

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Full access
Raúl P. Flores, Sabine Rijnsburger, Alexander R. Horner-Devine, Nirnimesh Kumar, Alejandro J. Souza, and Julie D. Pietrzak

Abstract

This study investigates the influence of tidal straining in the generation of turbidity maximum zones (TMZ), which are observed to extend for tens of kilometers along some shallow, open coastal seas. Idealized numerical simulations are conducted to reproduce the cross-shore dynamics and tidal straining in regions of freshwater influence (ROFIs), where elliptical current patterns are generated by the interaction between stratification and a tidal Kelvin wave. Model results show that tidal straining leads to cross-shore sediment convergence and the formation of a nearshore TMZ that is detached from the coastline. The subtidal landward sediment fluxes are created by asymmetries in vertical mixing between the stratifying and destratifying phases of the tidal cycle. This process is similar to the tidal straining mechanism that is observed in estuaries, except that in this case the convergence zone and TMZ are parallel to the shoreline and perpendicular to both the direction of the freshwater flux and the major axis of the tidal flow. We introduce the term minor axis tidal straining (MITS) to describe the tidal straining in these systems and to differentiate it from the tidal straining that occurs when the major axis of the tidal ellipse is aligned with the density gradient. The occurrence of tidal straining and the coastal TMZ is predicted in terms of the Simpson (Si) and Stokes (Stk) numbers, and top–bottom tidal ellipticity difference (Δε). Based on our results, we find that SiStk2 > 3 and Δε > 0.5 provide a limiting condition for the required density gradients and latitudes for the occurrence of MITS and the generation of a TMZ.

Open access