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Abstract
Adaptive multilevel methods allow full coupling of atmospheric and land surface hydrological models by preserving consistency between the large-scale (atmospheric) and the regional (land) components. The methodology was investigated for three case studies involving the coupling of models with different levels of complexity and different spatial resolutions. The first case study consisted of coupling two simple models. One model provided the potential and the other the rotational components of atmospheric wind fields, which were used to drive a 3D orographic precipitation model used to investigate the long-term precipitation for the Olympic Mountains in Washington State. In the second case study, intermittent coupling (every 4 hours) of three versions of the orographic precipitation model operating at 40-m, 60-m, and 80-km resolution, respectively, was established to replicate the precipitation patterns of specifically chosen storms as they evolved across the central Sierra Nevada region. The third case study consisted of coupling the orographic precipitation model (40-km resolution) to a 1D model describing mass and energy balance conditions at the land surface for the northern and central Sierra Nevada region. Numerical coupling of the precipitation and the land surface models was implemented on a 2D finite-element mesh with 10-km resolution. One contribution of this study was the long-term simulation of the intra-annual dynamics of the hydrological cycle in a mountainous environment.
Abstract
Adaptive multilevel methods allow full coupling of atmospheric and land surface hydrological models by preserving consistency between the large-scale (atmospheric) and the regional (land) components. The methodology was investigated for three case studies involving the coupling of models with different levels of complexity and different spatial resolutions. The first case study consisted of coupling two simple models. One model provided the potential and the other the rotational components of atmospheric wind fields, which were used to drive a 3D orographic precipitation model used to investigate the long-term precipitation for the Olympic Mountains in Washington State. In the second case study, intermittent coupling (every 4 hours) of three versions of the orographic precipitation model operating at 40-m, 60-m, and 80-km resolution, respectively, was established to replicate the precipitation patterns of specifically chosen storms as they evolved across the central Sierra Nevada region. The third case study consisted of coupling the orographic precipitation model (40-km resolution) to a 1D model describing mass and energy balance conditions at the land surface for the northern and central Sierra Nevada region. Numerical coupling of the precipitation and the land surface models was implemented on a 2D finite-element mesh with 10-km resolution. One contribution of this study was the long-term simulation of the intra-annual dynamics of the hydrological cycle in a mountainous environment.
Shallow Precipitation Detection and Classification Using Multifrequency Radar Observations and Model SimulationsAbstract
Detection of shallow warm rainfall remains a critical source of uncertainty in remote sensing of precipitation, especially in regions of complex topographic and radiometric transitions, such as mountains and coastlines. To address this problem, a new algorithm to detect and classify shallow rainfall based on space–time dual-frequency correlation (DFC) of concurrent W- and Ka-band radar reflectivity profiles is demonstrated using ground-based observations from the Integrated Precipitation and Hydrology Experiment (IPHEx) in the Appalachian Mountains (MV), United States, and the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% for MV and TMP, respectively, corresponding to one order of magnitude improvement over the skill of operational satellite-based radar algorithms in similar conditions. Shallow rainfall is misclassified 12.5% of the time at MV, but all instances of low-level reverse orographic enhancement are detected and classified correctly. The classification errors are 8% and 17% for deep and shallow rainfall, respectively, in TMP; the latter is linked to reflectivity profiles with dark band but insufficient radar sensitivity to light rainfall (
Abstract
Detection of shallow warm rainfall remains a critical source of uncertainty in remote sensing of precipitation, especially in regions of complex topographic and radiometric transitions, such as mountains and coastlines. To address this problem, a new algorithm to detect and classify shallow rainfall based on space–time dual-frequency correlation (DFC) of concurrent W- and Ka-band radar reflectivity profiles is demonstrated using ground-based observations from the Integrated Precipitation and Hydrology Experiment (IPHEx) in the Appalachian Mountains (MV), United States, and the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% for MV and TMP, respectively, corresponding to one order of magnitude improvement over the skill of operational satellite-based radar algorithms in similar conditions. Shallow rainfall is misclassified 12.5% of the time at MV, but all instances of low-level reverse orographic enhancement are detected and classified correctly. The classification errors are 8% and 17% for deep and shallow rainfall, respectively, in TMP; the latter is linked to reflectivity profiles with dark band but insufficient radar sensitivity to light rainfall (
Abstract
The contribution of surface evapotranspiration (ET) to moist convection, cloudiness, and precipitation along the eastern flanks of the tropical Andes (EADS) was investigated using the Weather Research and Forecasting (WRF) Model with nested simulations of selected weather conditions down to 1.2-km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby, at every time step, the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere. Energy balance analysis indicates that surface ET influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to approximately 60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven or under conditionally stable conditions, the role of latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of surface ET, this process operates in concert with damped convective energy, suppressing cloudiness and decreasing daily precipitation by up to around 50% in the simulations presented here.
Abstract
The contribution of surface evapotranspiration (ET) to moist convection, cloudiness, and precipitation along the eastern flanks of the tropical Andes (EADS) was investigated using the Weather Research and Forecasting (WRF) Model with nested simulations of selected weather conditions down to 1.2-km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby, at every time step, the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere. Energy balance analysis indicates that surface ET influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to approximately 60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven or under conditionally stable conditions, the role of latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of surface ET, this process operates in concert with damped convective energy, suppressing cloudiness and decreasing daily precipitation by up to around 50% in the simulations presented here.
Abstract
The objective of this study is to estimate the vertical structure of the latent heating of precipitation in the vicinity of the Himalayas. Based on a cloud physics parameterization and the thermodynamic equilibrium equation, a simple algorithm is proposed to estimate latent heating from a combination of radiosonde and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data, specifically, the radar reflectivity and the rain-rate estimates. An evaluation of the algorithm against 6-hourly areal averages from diagnostic budget studies during the South China Sea Monsoon Experiment (SCMEX) suggests that the algorithm captures well the vertical structure of latent heating between the top of the moist layer and the cloud-top detrainment layer. The retrieval algorithm was applied systematically over the Indian subcontinent and Tibetan plateau within a region comprising 15°–32°N and 70°–95°E during June, the month of monsoon onset, for three different years (1999, 2000, and 2001). The estimated latent heating profiles exhibit large spatial and temporal variability in the magnitude and position of maximum latent heating within the same TRMM overpass, and from one year to the next. This reflects the presence of convective activity with varying degrees of organization during the monsoon, and also the interannual variability of large-scale conditions. Along the Himalayan range, the diurnal cycle of latent heating profiles suggests more intense convective activity in the early morning and during nighttime (1-km difference in the height of maximum latent heating), consistent with the diurnal cycle of rainfall observations and cloudiness. The height of maximum latent heating at stations in the Indian subcontinent varies over a wide range, reflecting a mix of stratiform and convective precipitation systems, respectively, 5.7 ± 2, 3.8 ± 1.5, and 4.8 ± 1.7 km MSL, for 1999, 2000, and 2001. Overall, the peak production of latent heating is roughly at the effective terrain elevation of the Himalayan range with regard to synoptic circulation and orographic enhancement effects. The Tibetan plateau behaves as an elevated heat source with maximum heating produced at 7–8 km MSL. Average values of the maximum latent heating ranged between 1.3 and 1.6 K day−1 per unit rainfall (1 cm day−1), with maximum values of up to 10 K day−1.
Abstract
The objective of this study is to estimate the vertical structure of the latent heating of precipitation in the vicinity of the Himalayas. Based on a cloud physics parameterization and the thermodynamic equilibrium equation, a simple algorithm is proposed to estimate latent heating from a combination of radiosonde and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data, specifically, the radar reflectivity and the rain-rate estimates. An evaluation of the algorithm against 6-hourly areal averages from diagnostic budget studies during the South China Sea Monsoon Experiment (SCMEX) suggests that the algorithm captures well the vertical structure of latent heating between the top of the moist layer and the cloud-top detrainment layer. The retrieval algorithm was applied systematically over the Indian subcontinent and Tibetan plateau within a region comprising 15°–32°N and 70°–95°E during June, the month of monsoon onset, for three different years (1999, 2000, and 2001). The estimated latent heating profiles exhibit large spatial and temporal variability in the magnitude and position of maximum latent heating within the same TRMM overpass, and from one year to the next. This reflects the presence of convective activity with varying degrees of organization during the monsoon, and also the interannual variability of large-scale conditions. Along the Himalayan range, the diurnal cycle of latent heating profiles suggests more intense convective activity in the early morning and during nighttime (1-km difference in the height of maximum latent heating), consistent with the diurnal cycle of rainfall observations and cloudiness. The height of maximum latent heating at stations in the Indian subcontinent varies over a wide range, reflecting a mix of stratiform and convective precipitation systems, respectively, 5.7 ± 2, 3.8 ± 1.5, and 4.8 ± 1.7 km MSL, for 1999, 2000, and 2001. Overall, the peak production of latent heating is roughly at the effective terrain elevation of the Himalayan range with regard to synoptic circulation and orographic enhancement effects. The Tibetan plateau behaves as an elevated heat source with maximum heating produced at 7–8 km MSL. Average values of the maximum latent heating ranged between 1.3 and 1.6 K day−1 per unit rainfall (1 cm day−1), with maximum values of up to 10 K day−1.
Abstract
The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.
Abstract
The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.
Abstract
Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone—National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]—for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.
Abstract
Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone—National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]—for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.
Abstract
A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h−1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.
Abstract
A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h−1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.
Abstract
The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L ≫ l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (∼25-km grid spacing) to the same resolution as the NCEP stage IV products (∼4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied.
Abstract
The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L ≫ l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (∼25-km grid spacing) to the same resolution as the NCEP stage IV products (∼4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied.
Abstract
Precipitation in remote mountainous areas dominates the water balance of many water-short areas of the globe, such as western North America. The inaccessibility of such environments prevents adequate measurement of the spatial distribution of precipitation and, hence, direct estimation of the water balance from observations of precipitation and runoff. Resolution constraints in atmospheric models can likewise result in large biases in prediction of the water balance for grid cells that include highly diverse topography. Modeling of the advection of moisture over topographic barriers at a spatial scale sufficient to resolve the dominant topographic features offers one method of better predicting the spatial distribution of precipitation in mountainous areas. A model is described herein that simulates Lagrangian transport of moist static energy and total water through a 3D finite-element grid, where precipitation is the only scavenging agent of both variables. The model is aimed primarily at the reproduction of the properties of high-elevation precipitation for long periods of time, but it operates at a time scale (during storm periods) of 10 min to 1 h and, therefore, is also able to reproduce the distribution of storm precipitation with an accuracy that may make it appropriate for the forecasting of extreme events. The model was tested by application to the Olympic Mountains, Washington, for a period of eight years (1967–74). Areal average precipitation, estimated through use of seasonal and annual runoff, was reproduced with errors in the 10%–15% range. Similar accuracy was achieved using point estimates of monthly precipitation from snow courses and low-elevation precipitation gauges.
Abstract
Precipitation in remote mountainous areas dominates the water balance of many water-short areas of the globe, such as western North America. The inaccessibility of such environments prevents adequate measurement of the spatial distribution of precipitation and, hence, direct estimation of the water balance from observations of precipitation and runoff. Resolution constraints in atmospheric models can likewise result in large biases in prediction of the water balance for grid cells that include highly diverse topography. Modeling of the advection of moisture over topographic barriers at a spatial scale sufficient to resolve the dominant topographic features offers one method of better predicting the spatial distribution of precipitation in mountainous areas. A model is described herein that simulates Lagrangian transport of moist static energy and total water through a 3D finite-element grid, where precipitation is the only scavenging agent of both variables. The model is aimed primarily at the reproduction of the properties of high-elevation precipitation for long periods of time, but it operates at a time scale (during storm periods) of 10 min to 1 h and, therefore, is also able to reproduce the distribution of storm precipitation with an accuracy that may make it appropriate for the forecasting of extreme events. The model was tested by application to the Olympic Mountains, Washington, for a period of eight years (1967–74). Areal average precipitation, estimated through use of seasonal and annual runoff, was reproduced with errors in the 10%–15% range. Similar accuracy was achieved using point estimates of monthly precipitation from snow courses and low-elevation precipitation gauges.
