scholarly journals Evaluation of Mekong River Commission operational flood forecasts, 2000–2012

2013 ◽  
Vol 10 (11) ◽  
pp. 14433-14461 ◽  
Author(s):  
T. C. Pagano
Keyword(s):  
Protective Action ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Mekong River ◽  
Average Error ◽  
Wet Season ◽  
Phnom Penh ◽  
Mekong River Commission ◽  
Flood Level ◽  
Skill Scores

Abstract. This study created a 13 yr historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1 to 5 day-ahead daily deterministic river height forecasts for 22 locations throughout the wet season (June–October). When these forecasts reach near Flood Level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g. 1 day-ahead Nash–Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. 5 day-ahead forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The Coefficients of Persistence for 1 day-ahead forecasts are typically 0.4–0.8 and 5 day-ahead forecasts are typically 0.1–0.7. RFMMC uses a series of benchmarks to define a metric of Percentage Satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead-times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13 yr period. There are no obvious trends in the Percentage of Satisfactory forecasts from 2002–2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day-ahead, 31% at 5 day-ahead) and false alarm rate (13% at 1 day-ahead, 74% at 5 days-ahead).

scholarly journals Evaluation of Mekong River commission operational flood forecasts, 2000–2012

2014 ◽  
Vol 18 (7) ◽  
pp. 2645-2656 ◽  
Author(s):  
T. C. Pagano
Keyword(s):  
Protective Action ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Mekong River ◽  
Average Error ◽  
Wet Season ◽  
Phnom Penh ◽  
Mekong River Commission ◽  
Flood Level ◽  
Skill Scores

Abstract. This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June–October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash–Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4–0.8 and 5-day forecasts are typically 0.1–0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

scholarly journals Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 683
Author(s):  
Mark DeMaria ◽  
James L. Franklin ◽  
Matthew J. Onderlinde ◽  
John Kaplan
Keyword(s):  
Tropical Cyclone ◽  
Absolute Error ◽  
Baseline Period ◽  
Probability Of Detection ◽  
Rapid Intensification ◽  
Improvement Program ◽  
Deterministic Models ◽  
Operational Forecasting ◽  
National Hurricane Center ◽  
Made In

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period.

scholarly journals Changing Land Use and Population Density Are Degrading Water Quality in the Lower Mekong Basin

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1948
Author(s):  
Flavia Tromboni ◽  
Thomas E. Dilts ◽  
Sarah E. Null ◽  
Sapana Lohani ◽  
Peng Bun Ngor ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Population Density ◽  
Local Population ◽  
Reference Conditions ◽  
Mekong River ◽  
Land Use Impacts ◽  
Wet Season ◽  
Mekong Basin ◽  
Lower Mekong Basin

Establishing reference conditions in rivers is important to understand environmental change and protect ecosystem integrity. Ranked third globally for fish biodiversity, the Mekong River has the world’s largest inland fishery providing livelihoods, food security, and protein to the local population. It is therefore of paramount importance to maintain the water quality and biotic integrity of this ecosystem. We analyzed land use impacts on water quality constituents (TSS, TN, TP, DO, NO3−, NH4+, PO43−) in the Lower Mekong Basin. We then used a best-model regression approach with anthropogenic land-use as independent variables and water quality parameters as the dependent variables, to define reference conditions in the absence of human activities (corresponding to the intercept value). From 2000–2017, the population and the percentage of crop, rice, and plantation land cover increased, while there was a decrease in upland forest and flooded forest. Agriculture, urbanization, and population density were associated with decreasing water quality health in the Lower Mekong Basin. In several sites, Thailand and Laos had higher TN, NO3−, and NH4+ concentrations compared to reference conditions, while Cambodia had higher TP values than reference conditions, showing water quality degradation. TSS was higher than reference conditions in the dry season in Cambodia, but was lower than reference values in the wet season in Thailand and Laos. This study shows how deforestation from agriculture conversion and increasing urbanization pressure causes water quality decline in the Lower Mekong Basin, and provides a first characterization of reference water quality conditions for the Lower Mekong River and its tributaries.

scholarly journals Using Fractal Downscaling of Satellite Precipitation Products for Hydrometeorological Applications

2010 ◽  
Vol 27 (3) ◽  
pp. 409-427 ◽  
Author(s):  
Kun Tao ◽  
Ana P. Barros
Keyword(s):  
Spatial Resolution ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Skill Score ◽  
Probability Of Detection ◽  
Grid Spacing ◽  
Satellite Precipitation ◽  
Target Field ◽  
Skill Scores ◽  
Central United States

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.

scholarly journals Validation of the METEOSAT storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa

2013 ◽  
Vol 6 (1) ◽  
pp. 1269-1310 ◽  
Author(s):  
T. Zinner ◽  
C. Forster ◽  
E. de Coning ◽  
H.-D. Betz
Keyword(s):  
South Africa ◽  
Thermal Emission ◽  
Climatic Conditions ◽  
Probability Of Detection ◽  
Lightning Activity ◽  
Network Data ◽  
Detection Scheme ◽  
Lightning Detection ◽  
Skill Scores ◽  
Physical Limitations

Abstract. In this manuscript, recent changes to the DLR METEOSAT thunderstorm TRacking And Monitoring algorithm (Cb-TRAM) are presented as well as a validation of Cb-TRAM against the European ground-based LIghtning NETwork data (LINET) of Nowcast GmbH and Lightning Detection Network (LDN) data of the South African Weather Service (SAWS). The validation is conducted along the well known skill scores probability of detection (POD) and false alarm ratio (FAR) on the basis of METEOSAT/SEVIRI pixels as well as on the basis of thunderstorm objects. The values obtained demonstrate the limits of Cb-TRAM in specific as well as the limits of satellite methods in general which are based on thermal emission and solar reflectivity information from thunderstorm tops. Although the climatic conditions and the occurence of thunderstorms is quite different for Europe and South Africa, the quality score values are similar. Our conclusion is that Cb-TRAM provides robust results of well-defined quality for very different climatic regimes. The POD for a thunderstorm with intense lightning is about 80% during the day. The FAR for a Cb-TRAM detected thunderstorm which is not at least close to intense lightning activity is about 50%; if the proximity to any lightning activity is evaluated the FAR is even much lower at about 15%. Pixel-based analysis shows that the detected thunderstorm object size is not indiscriminately large, but well within the physical limitations of the method. Nighttime POD and FAR are somewhat worse as the detection scheme can not use high resolution visible information. Nowcasting scores show useful values up to approximatelly 30 min.

scholarly journals Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 83-90
Author(s):  
PIYUSH JOSHI ◽  
M.S. SHEKHAR ◽  
ASHAVANI KUMAR ◽  
J.K. QUAMARA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
India Meteorological Department ◽  
Western Himalaya ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ann Model ◽  
Skill Scores ◽  
Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

scholarly journals Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground based weather radar network over Sweden

2015 ◽  
Vol 8 (8) ◽  
pp. 8157-8189
Author(s):  
L. Norin ◽  
A. Devasthale ◽  
T. S. L'Ecuyer ◽  
N. B. Wood ◽  
M. Smalley
Keyword(s):  
High Latitude ◽  
Precipitation Amount ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Polar Regions ◽  
Rain Gauges ◽  
Radar Network ◽  
Skill Scores ◽  
Satellite Sensors ◽  
Observing Systems

Abstract. To be able to estimate snowfall accurately is important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain-gauges to estimate precipitation in this context. The Cloud Profiling Radar (CPR) onboard CloudSat is especially proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and ability to provide near-global vertical structure. The importance of having snowfall estimates from CloudSat/CPR further increases in the high latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. Here we intercompared snowfall estimates from two observing systems, CloudSat and Swerad, the Swedish national weather radar network. Swerad offers one of the best calibrated data sets of precipitation amount at very high latitudes that are anchored to rain-gauges and that can be exploited to evaluate usefulness of CloudSat/CPR snowfall estimates in the polar regions. In total 7.2×105 matchups of CloudSat and Swerad over Sweden were inter-compared covering all but summer months (October to May) from 2008 to 2010. The intercomparison shows encouraging agreement between these two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46–82 km), when the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station as Swerad's sensitivity decreases as a function of distance and Swerad also tends to overshoots low level precipitating systems further away from the station, leading to underestimation of snowfall rate and occasionally missing the precipitation altogether. Further investigations of various statistical metrics, such as the probability of detection, false alarm rate, hit rate, and the Hanssen–Kuipers skill scores, and the sensitivity of these metrics to snowfall rate and the distance from the radar station, were carried out. The results of these investigations highlight the strengths and the limitations of both observing systems at the lower and upper ends of snowfall distributions and the range of uncertainties that could be expected from these systems in the high latitude regions.

scholarly journals A Device to Measure a Smoker’s Puffing Topography and Real-Time Puff-By-Puff “Tar” Delivery

2014 ◽  
Vol 26 (2) ◽  
Author(s):  
Sandra J. Slayford ◽  
Barrie E. Frost
Keyword(s):  
Real Time ◽  
Light Emitting Diode ◽  
Absolute Error ◽  
Average Error ◽  
Light Emitting ◽  
Light Obscuration ◽  
Test Study ◽  
Added Benefit ◽  
Volume Characteristics ◽  
The Relationship

AbstractA device for measuring the flow, duration and volume characteristics of human puffing behaviour when smoking cigarettes is described. Cigarettes are smoked through a holder comprising a measured pressure drop across a critical orifice. The holder also contains a Light Emitting Diode (LED) and photodetector that measures light obscuration in order to estimate nicotine-free dry particulate matter (NFDPM, “tar”) delivery. All data are recorded on a puff-by-puff basis and displayed in real time. These NFDPM estimates are known as optical “tar” (OT), and are derived from the calibration of the OT measurement versus gravimetric NFDPM yields of cigarettes under a range of smoking regimes. In a test study, puff volumes from 20-80 mL were recorded to ± 6.0% of a pre-set volume, with an absolute error of 4.7 mL for an 80 mL volume drawn on a lit cigarette, and an average error of less than 2.0 mL across the range 20-80 mL. The relationship between NFDPM and OT was linear (R2 = 0.99) and accurate to ± 1.3 mg per cigarette over the range 1-23 mg per cigarette. The device provides an alternative to the widely used part filter methodology for estimating mouth level exposure with an added benefit that no further laboratory smoking replication or analysis is required. When used in conjunction with the part filter methodology, the puffing behaviour recorded can explain anomalies in the data while providing a second independent estimate.

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