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Abstract
Monin-Obukhov similarity was used to calculate sensible heat fluxes (H c ) at an array of up to 20 surface flux measurement sites on five days in 1987 and 1989 during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment by means of spatially distributed radiometric surface temperatures from an airborne platform and ground-based data. To use Monin-Obukhov similarity, a parameterization for the scalar roughness, as a function of spatially varying leaf area index (LAI) and friction velocity (u∗), was developed from a previous, simpler parameterization. LAI was found to be significant, but the range of u∗ was too small to ascertain its significance. The parameterization was found to produce sensible heat flux values that had correlations around 0.8 with the spatially distributed sensible heat flux measurements on four of the days, but on a day with high, uniform soil moisture content, the correlation was only 0.226. It is argued that the high soil moisture values indirectly resulted in relatively larger significance of noise in the surface–air temperature difference, which reduced the reliability of the calculated sensible heat fluxes. In addition, constants in the parameterization from one day may not necessarily be applicable to other days. This may be due to factors such as solar elevation and instrument view angle. It is proposed and verified that the differences between dates can be resolved in a spatially averaged sense by accounting for the effects of seasonal variation in solar elevation on the vertical distribution of canopy temperatures. This produced a correlation of 0.973 between measured and calculated sensible heat fluxes when all dates were considered simultaneously.
Abstract
Monin-Obukhov similarity was used to calculate sensible heat fluxes (H c ) at an array of up to 20 surface flux measurement sites on five days in 1987 and 1989 during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment by means of spatially distributed radiometric surface temperatures from an airborne platform and ground-based data. To use Monin-Obukhov similarity, a parameterization for the scalar roughness, as a function of spatially varying leaf area index (LAI) and friction velocity (u∗), was developed from a previous, simpler parameterization. LAI was found to be significant, but the range of u∗ was too small to ascertain its significance. The parameterization was found to produce sensible heat flux values that had correlations around 0.8 with the spatially distributed sensible heat flux measurements on four of the days, but on a day with high, uniform soil moisture content, the correlation was only 0.226. It is argued that the high soil moisture values indirectly resulted in relatively larger significance of noise in the surface–air temperature difference, which reduced the reliability of the calculated sensible heat fluxes. In addition, constants in the parameterization from one day may not necessarily be applicable to other days. This may be due to factors such as solar elevation and instrument view angle. It is proposed and verified that the differences between dates can be resolved in a spatially averaged sense by accounting for the effects of seasonal variation in solar elevation on the vertical distribution of canopy temperatures. This produced a correlation of 0.973 between measured and calculated sensible heat fluxes when all dates were considered simultaneously.
Abstract
Spatial and temporal variability of relative humidity over the West African monsoon (WAM) region is investigated. In particular, the variability during the onset and retreat periods of the monsoon is considered. A K-means cluster analysis was performed to identify spatially coherent regions of relative humidity variability during the two periods. The cluster average of the relative humidity provides a robust representative index of the strength and timing of the transition periods between the dry and wet periods. Correlating the cluster indices with large-scale circulation and sea surface temperatures indicates that the land–ocean temperature gradient and the corresponding circulation, tropical Atlantic sea surface temperatures (SSTs), and to a somewhat lesser extent tropical Pacific SSTs all play a role in modulating the timing of the monsoon season relative humidity onset and retreat. These connections to large-scale climate features were also found to be persistent over interseasonal time scales, and thus best linear predictive models were developed to enable skillful forecasts of relative humidity during the two periods at 15–75-day lead times. The public health risks due to meningitis epidemics are of grave concern to the population in this region, and these risks are strongly tied to regional humidity levels. Because of this linkage, the understanding and predictability of relative humidity variability is of use in meningitis epidemic risk mitigation, which motivated this research.
Abstract
Spatial and temporal variability of relative humidity over the West African monsoon (WAM) region is investigated. In particular, the variability during the onset and retreat periods of the monsoon is considered. A K-means cluster analysis was performed to identify spatially coherent regions of relative humidity variability during the two periods. The cluster average of the relative humidity provides a robust representative index of the strength and timing of the transition periods between the dry and wet periods. Correlating the cluster indices with large-scale circulation and sea surface temperatures indicates that the land–ocean temperature gradient and the corresponding circulation, tropical Atlantic sea surface temperatures (SSTs), and to a somewhat lesser extent tropical Pacific SSTs all play a role in modulating the timing of the monsoon season relative humidity onset and retreat. These connections to large-scale climate features were also found to be persistent over interseasonal time scales, and thus best linear predictive models were developed to enable skillful forecasts of relative humidity during the two periods at 15–75-day lead times. The public health risks due to meningitis epidemics are of grave concern to the population in this region, and these risks are strongly tied to regional humidity levels. Because of this linkage, the understanding and predictability of relative humidity variability is of use in meningitis epidemic risk mitigation, which motivated this research.
Abstract
This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are presented for the summers of 2003–07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents.
Abstract
This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are presented for the summers of 2003–07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents.
Abstract
This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.
Abstract
This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.
Abstract
The variation of relative humidity across West Africa during the dry season is evaluated using the Modern Era Retrospective Analysis for Research and Applications (MERRA) dataset and the method of self-organizing maps. Interest in the dry season of West Africa is related to the connection between near-surface atmospheric moisture and the occurrence of meningitis across West Africa, most notably in the region known as the meningitis belt. The patterns in relative humidity are analyzed in terms of frequency of each pattern as well as the sequencing from one pattern to the next. The variations in relative humidity are characterized subannually for individual years from 1979 to 2009 as well as decadally over the entire 30-yr duration of dry seasons in West Africa. The progression from relatively moist patterns to relatively dry patterns and back to the moist patterns over the course of the dry season corresponds to the northward and then southward migration of the intertropical convergence zone. The results indicate distinctly different frequency and sequencing of relative humidity patterns from year to year. The year-to-year changes in relative humidity patterns are gradual. There is some indication of a larger, possibly decadal, pattern to the year-to-year changes in the variation of relative humidity over the course of the dry season. The results are reflective of the reanalysis data including potentially unusual and erroneously dry conditions in central Africa after the mid-1990s.
Abstract
The variation of relative humidity across West Africa during the dry season is evaluated using the Modern Era Retrospective Analysis for Research and Applications (MERRA) dataset and the method of self-organizing maps. Interest in the dry season of West Africa is related to the connection between near-surface atmospheric moisture and the occurrence of meningitis across West Africa, most notably in the region known as the meningitis belt. The patterns in relative humidity are analyzed in terms of frequency of each pattern as well as the sequencing from one pattern to the next. The variations in relative humidity are characterized subannually for individual years from 1979 to 2009 as well as decadally over the entire 30-yr duration of dry seasons in West Africa. The progression from relatively moist patterns to relatively dry patterns and back to the moist patterns over the course of the dry season corresponds to the northward and then southward migration of the intertropical convergence zone. The results indicate distinctly different frequency and sequencing of relative humidity patterns from year to year. The year-to-year changes in relative humidity patterns are gradual. There is some indication of a larger, possibly decadal, pattern to the year-to-year changes in the variation of relative humidity over the course of the dry season. The results are reflective of the reanalysis data including potentially unusual and erroneously dry conditions in central Africa after the mid-1990s.
Abstract
This study identifies conditions that determine errors in numerical simulations of 10-m wind speed over moderately complex terrain, emphasizing winds that lead to overhead power-line damage over a subregion of the northeast United States. Simulations with the Mellor–Yamada–Janjić (MYJ) scheme, the Yonsei University (YSU) scheme, and a subgrid-scale topographic drag correction (Topo) applied to YSU are used to investigate error components. The wind speed distribution is dominated by low speeds, which are well depicted by Topo, but are underestimated by the MYJ and YSU schemes. Conversely, moderate and high speeds are underestimated by Topo, and MYJ and YSU perform better across specific ranges. Verification samples are conditioned by season, diurnal cycle, topography, and spatial patterns obtained with a clustering analysis. The systematic error is characterized by a positive bias in low speeds, and as speed increases the biases become more negative. Quantile comparisons, along with systematic and random errors, indicate that beyond the dependence on wind speed itself, errors also depend on seasonal characteristics, indirectly defined by scheme stability profiles. The positive relationship between absolute bias and speed originates in the friction velocity parameterization, and the correction for drag in the Topo scheme exacerbates the effect. The Topo scheme adjusts the total bias and sharpens the bias spread but penalizes moderate and high winds. Clusters reveal that in Topo the bias is primarily driven by wind direction. Excessive correction occurs on terrain-interacting flows, and oceanic flow modulates the adjustment, enhancing the scheme’s performance.
Abstract
This study identifies conditions that determine errors in numerical simulations of 10-m wind speed over moderately complex terrain, emphasizing winds that lead to overhead power-line damage over a subregion of the northeast United States. Simulations with the Mellor–Yamada–Janjić (MYJ) scheme, the Yonsei University (YSU) scheme, and a subgrid-scale topographic drag correction (Topo) applied to YSU are used to investigate error components. The wind speed distribution is dominated by low speeds, which are well depicted by Topo, but are underestimated by the MYJ and YSU schemes. Conversely, moderate and high speeds are underestimated by Topo, and MYJ and YSU perform better across specific ranges. Verification samples are conditioned by season, diurnal cycle, topography, and spatial patterns obtained with a clustering analysis. The systematic error is characterized by a positive bias in low speeds, and as speed increases the biases become more negative. Quantile comparisons, along with systematic and random errors, indicate that beyond the dependence on wind speed itself, errors also depend on seasonal characteristics, indirectly defined by scheme stability profiles. The positive relationship between absolute bias and speed originates in the friction velocity parameterization, and the correction for drag in the Topo scheme exacerbates the effect. The Topo scheme adjusts the total bias and sharpens the bias spread but penalizes moderate and high winds. Clusters reveal that in Topo the bias is primarily driven by wind direction. Excessive correction occurs on terrain-interacting flows, and oceanic flow modulates the adjustment, enhancing the scheme’s performance.
Abstract
Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method’s ability (to find good analogs) from the training set’s ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.
Abstract
Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method’s ability (to find good analogs) from the training set’s ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.
Abstract
Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.
Abstract
Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
