Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.

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Spatial Bias in Medium-Range Forecasts of Heavy Precipitation in the Sacramento River Basin: Implications for Water Management

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0226.1 ◽

2020 ◽

Vol 21 (7) ◽

pp. 1405-1423

Author(s):

Zachary P. Brodeur ◽

Scott Steinschneider

Keyword(s):

Forecast Error ◽

Heavy Precipitation ◽

Sacramento River ◽

Precipitation Forecast ◽

Lead Times ◽

Synoptic Scale ◽

Basin Scale ◽

Medium Range ◽

Precipitation Events ◽

Range Forecasts

AbstractForecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.

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The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

Monthly Weather Review ◽

10.1175/2010mwr3217.1 ◽

2010 ◽

Vol 138 (10) ◽

pp. 3787-3805 ◽

Cited By ~ 17

Author(s):

Arindam Chakraborty

Keyword(s):

Surface Temperature ◽

Diurnal Cycle ◽

Lead Time ◽

Tropical Convection ◽

Precipitation Forecast ◽

Lead Times ◽

Near Surface ◽

Forecast Lead Time ◽

Medium Range ◽

Ecmwf Model

Abstract This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4–5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air–sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with “zero” precipitation and heavy (>40 mm day−1) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2–4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

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Some Characteristics of Model-Predicted Precipitation during the Summer Monsoon over India

Journal of Applied Meteorology ◽

10.1175/jam-2198.1 ◽

2005 ◽

Vol 44 (3) ◽

pp. 324-339 ◽

Cited By ~ 13

Author(s):

B. K. Basu

Keyword(s):

Summer Monsoon ◽

Lead Time ◽

Precipitation Pattern ◽

Spatial Average ◽

Spatial Averaging ◽

Forecast Lead Time ◽

Medium Range ◽

Ecmwf Model ◽

Rainy Days ◽

Range Forecasts

Abstract For the summer monsoon seasons of 1995, 1996, and 1997 the day-1 to day-4 forecasts of precipitation from both the National Centre for Medium Range Weather Forecasting (NCMRWF) and the European Centre for Medium-Range Forecasts (ECMWF) models reproduce the main features of the observed precipitation pattern when averaged over the whole season. On average, less than 30% of all rain gauge stations in India report rain on a given day during the monsoon season. The number of observed rainy days increases to 41% after spatial averaging over ECMWF model grid boxes and to 50% after spatial averaging over NCMRWF model grid boxes. The NCMRWF model forecasts have 10%–15% more rainy days, mostly in the light or moderate precipitation categories, when compared with the spatial average of observed values. Seasonal accumulated values of all of India’s average precipitation show a slight increase with the forecast lead time for the NCMRWF model and a small decrease for the ECMWF model. The weekly accumulated values of forecast precipitation from both models, averaged over the whole of India, are in good phase relationship (∼0.9 in most cases) with the observed value for forecasts with a lead time up to day 4. Values of statistical parameters, based on the frequency of occurrence in various classes, indicate that the NCMRWF model has some skill in predicting precipitation over India during the summer monsoon. The NCMRWF model forecasts have higher trend correlation with the observed precipitation over India than do the ECMWF model forecasts. The mean error in precipitation is, however, much less in the ECMWF model forecasts, and the spatial distribution of seasonal average medium-range forecasts of ECMWF is closer to that observed along the west coast mountain ridgeline.

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Evaluating sub-seasonal heatwave reforecasts of the ECMWF over Europe

10.5194/ems2021-146 ◽

2021 ◽

Author(s):

Natalia Korhonen ◽

Otto Hyvärinen ◽

Matti Kämäräinen ◽

Kirsti Jylhä

Keyword(s):

Heat Wave ◽

Heat Waves ◽

Lead Times ◽

Weather Forecasts ◽

Wave Forecast ◽

Extended Range ◽

Hot Days ◽

Medium Range ◽

Summer Temperatures

Severe heatwaves have harmful impacts on ecosystems and society. Early warning of heat waves help with decreasing their harmful impact. Previous research shows that the Extended Range Forecasts (ERF) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have over Europe a somewhat higher reforecast skill for extreme hot summer temperatures than for long-term mean temperatures. Also it has been shown that the reforecast skill of the ERFs of the ECMWF was strongly increased by the most severe heat waves (the European heatwave 2003 and the Russian heatwave 2010).Our aim is to be able to estimate the skill of a heat wave forecast at the time the forecast is given. For that we investigated the spatial and temporal reforecast skill of the ERFs of the ECMWF to forecast hot days (here defined as a day on which the 5 days running mean surface temperature is above its summer 90th percentile) in the continental Europe in summers 2000-2019. We used the ECMWF 2-meter temperature reforecasts and verified them against the ERA5 reanalysis. The skill of the hot day reforecasts was estimated by the symmetric extremal dependence index (SEDI) which considers both hit rates and false alarm rates of the hot day forecasts. Further, we investigated the skill of the heatwave reforecasts based on at which time steps of the forecast the hot days were forecasted. We found that on the mesoscale (horizontal scale of ~500 km) the ERFs of the ECMWF were most skillful in predicting the life cycle of a heat wave (lasting up to 25 days) about a week before its start and during its course. That is, on the mesoscale those reforecasts, in which hot day(s) were forecasted to occur during the first 7&#8230;11 days, were more skillful on lead times up to 25 days than the rest of the heat wave forecasts. This finding is valuable information, e.g., in the energy and health sectors while preparing for a coming heat wave.The work presented here is part of the research project HEATCLIM (Heat and health in the changing climate) funded by the Academy of Finland.

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A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

Journal of Hydrometeorology ◽

10.1175/2011jhm1347.1 ◽

2011 ◽

Vol 12 (5) ◽

pp. 713-728 ◽

Cited By ~ 122

Author(s):

Lan Cuo ◽

Thomas C. Pagano ◽

Q. J. Wang

Keyword(s):

Lead Time ◽

Weather Prediction ◽

Research Field ◽

Lead Times ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

Forecast Lead Time ◽

Quantitative Precipitation Forecasts ◽

Active Research ◽

Nwp Model

Abstract Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

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On reliability analysis of multi-categorical forecasts

Nonlinear Processes in Geophysics ◽

10.5194/npg-15-661-2008 ◽

2008 ◽

Vol 15 (4) ◽

pp. 661-673 ◽

Cited By ~ 13

Author(s):

J. Bröcker

Keyword(s):

Reliability Analysis ◽

Goodness Of Fit ◽

Necessary Condition ◽

Lead Times ◽

Temperature Anomalies ◽

Probability Score ◽

Probabilistic Forecasts ◽

Medium Range ◽

Rank Histogram ◽

Range Forecasts

Abstract. Reliability analysis of probabilistic forecasts, in particular through the rank histogram or Talagrand diagram, is revisited. Two shortcomings are pointed out: Firstly, a uniform rank histogram is but a necessary condition for reliability. Secondly, if the forecast is assumed to be reliable, an indication is needed how far a histogram is expected to deviate from uniformity merely due to randomness. Concerning the first shortcoming, it is suggested that forecasts be grouped or stratified along suitable criteria, and that reliability is analyzed individually for each forecast stratum. A reliable forecast should have uniform histograms for all individual forecast strata, not only for all forecasts as a whole. As to the second shortcoming, instead of the observed frequencies, the probability of the observed frequency is plotted, providing and indication of the likelihood of the result under the hypothesis that the forecast is reliable. Furthermore, a Goodness-Of-Fit statistic is discussed which is essentially the reliability term of the Ignorance score. The discussed tools are applied to medium range forecasts for 2 m-temperature anomalies at several locations and lead times. The forecasts are stratified along the expected ranked probability score. Those forecasts which feature a high expected score turn out to be particularly unreliable.

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Empirical Correction of a Coupled Land–Atmosphere Model

Monthly Weather Review ◽

10.1175/2008mwr2344.1 ◽

2008 ◽

Vol 136 (11) ◽

pp. 4063-4076 ◽

Cited By ~ 16

Author(s):

Timothy DelSole ◽

Mei Zhao ◽

Paul A. Dirmeyer ◽

Ben P. Kirtman

Keyword(s):

Forecast Error ◽

Error Variance ◽

Correction Method ◽

Mean Value ◽

Lead Times ◽

Time Step ◽

Relaxation Methods ◽

Empirical Correction ◽

Atmosphere Model

Abstract This paper investigates empirical strategies for correcting the bias of a coupled land–atmosphere model and tests the hypothesis that a bias correction can improve the skill of such models. The correction strategies investigated include 1) relaxation methods, 2) nudging based on long-term biases, and 3) nudging based on tendency errors. The last method involves estimating the tendency errors of prognostic variables based on short forecasts—say lead times of 24 h or less—and then subtracting the climatological mean value of the tendency errors at every time step. By almost any measure, the best correction strategy is found to be nudging based on tendency errors. This method significantly reduces biases in the long-term forecasts of temperature and soil moisture, and preserves the variance of the forecast field, unlike relaxation methods. Tendency errors estimated from ten 1-day forecasts produced just as effective corrections as tendency errors estimated from all days in a month, implying that the method is trivial to implement by modern standards. Disappointingly, none of the methods investigated consistently improved the random error variance of the model, although this finding may be model dependent. Nevertheless, the empirical correction method is argued to be worthwhile even if it improves only the bias, because the method has only marginal impacts on the numerical speed and represents forecast error in the form of a tendency error that can be compared directly to other terms in the tendency equations, which in turn provides clues as to the source of the forecast error.

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Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir

Hydrology Research ◽

10.2166/nh.2017.085 ◽

2017 ◽

Vol 49 (5) ◽

pp. 1513-1527 ◽

Cited By ~ 19

Author(s):

Zhongmin Liang ◽

Tiantian Tang ◽

Binquan Li ◽

Tian Liu ◽

Jun Wang ◽

...

Keyword(s):

Hybrid Model ◽

Random Forests ◽

Lead Time ◽

Swat Model ◽

Monthly Precipitation ◽

Streamflow Forecasting ◽

Danjiangkou Reservoir ◽

Forecast Lead Time ◽

Reservoir Basin

Abstract Long-term streamflow forecasting is of great significance to the optimal management of water resources. However, the forecast lead time of long-term streamflow forecasting is relatively long and the forecasted precipitation within the forecast lead time has inherent uncertainty, so long-term streamflow forecasting has major challenges. In this paper, a hybrid forecasting model is developed to improve accuracy of long-term streamflow forecasting by combining random forests (RF) and the Soil and Water Assessment Tool (SWAT). The RF model is used to forecast monthly precipitation which is further downscaled to a daily dataset according to the hydrological similarity principle for use in the SWAT model of the Danjiangkou Reservoir basin, China. Performance of this hybrid model is compared to that of seasonal autoregressive (SAR (P)) model. Results show the RF precipitation generator yields accurate predictions at the monthly scale and the hybrid model produces acceptable streamflow series in long-term forecasting cases. In addition, the comparison shows that in the Danjiangkou Reservoir basin, the hybrid model performs better than the SAR (P) model, with average Nash–Sutcliffe efficiency (NSE) values of 0.94 and 0.51, which is better when it is closer to 1. This study provides a method of improving accuracy of long-term streamflow forecasting.

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Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0297.1 ◽

2021 ◽

Author(s):

Ganesh R. Ghimire ◽

Witold F. Krajewski ◽

Felipe Quintero

Keyword(s):

Real Time ◽

Time Scale ◽

Lead Time ◽

Time Horizon ◽

Streamflow Forecasting ◽

Forecasting Accuracy ◽

Trade Off ◽

Forecast Lead Time ◽

Medium Range ◽

Forecasting System

AbstractIncorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time (tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

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Evaluation of near-surface temperature forecasts against super-site observations

10.5194/egusphere-egu2020-15852 ◽

2020 ◽

Author(s):

Polly Schmederer ◽

Irina Sandu ◽

Thomas Haiden ◽

Anton Beljaars ◽

Martin Leutbecher ◽

...

Keyword(s):

Forecast Error ◽

Small Scale ◽

Forecast Errors ◽

Near Surface ◽

Surface Weather ◽

Medium Range ◽

Weather Parameters ◽

Temperature Errors

ECMWF&#8217;s medium-range forecasts of near-surface weather parameters, such as 2 m temperature, humidity and 10 m wind speed, have become more skilful over the years, following the trend of improvements in the forecast skill of upper-air fields. However, they are still affected by systematic errors which have proved difficult to eliminate. Systematic forecast errors in temperature and humidity near the surface can be better understood by also examining errors higher up in the atmospheric boundary layer and in the soil. Meteorological observatories, also known as super-sites, provide long-term observational records of such vertical profiles. ECMWF started to use data from super-sites more systematically to evaluate the quality of forecasts in the lowest part of the atmosphere (up to 100m) and in the soil, in an attempt to disentangle sources of forecast error in near-surface weather parameters. Findings for 2-metre temperature errors in ECMWF forecasts at European super-sites suggest that the errors are partly the result of the model exchanging too much energy between the atmosphere and the land. However, the influence of other factors, such as errors resulting from the representation of vegetation in semi-arid areas and from small-scale variations in vegetation and soil type near measurement stations, mean that it is difficult to adjust the energy exchange in a way which leads to an overall error reduction on the European scale.

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