A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.     

A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables

This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.     

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.     

Simulating the value of El Niño forecasts for the Panama Canal

The Panama Canal relies on rain-fed streamflow into Gatun Lake, the canal’s primary storage facility, for operations—principally ship passage and hydropower generation. Precipitation in much of Panama has a strong negative relationship with eastern tropical Pacific sea surface temperature (SST) and this relationship is reflected in Gatun Lake inflows. For example, the correlation coefficient between wet season (July–December) inflow and NINO3 SST is −0.53 over the period 1914–1997. Operational capabilities to predict tropical Pacific SSTs have been demonstrated by several forecast systems during the past decade, and (as we show) such SST forecasts can be used to reduce the uncertainty of estimates of future inflows (compared with climatological expectations). Because substantial reductions in lake inflow negatively impact canal operations, we wondered whether these forecasts of future inflows, coupled with a method for translating that information into effective operational policy, might result in more efficient canal management. A combined simulation/optimization/assessment “virtual” canal system was implemented and exercised using operational El Niño forecasts over the period 1981–1998. The results show the following main points:

(i)

At current demand levels, the canal system is relatively robust (insensitive to flow forecasts) unless flows are substantially reduced (i.e., during El Niño episodes) or forecasts are extremely accurate.     

Development,implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.     

The value of updating ensemble streamflow prediction in reservoir operations

This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Un exemple de collaboration internationale en hydrologie

Summary

Forecasts of the discharge of the Rhine at Basel have been studied in Zurich since 1954.

In this programme have cooperated hydro-power companies of Switzerland, Germany and France, French shipping-firms, and the Dutch water management authorities. For the Netherlands the forecast of the summer yield of the Rhine is of great concern, since it is the main resource of fresh water.

The studies have resulted in:

— since 1955, annual forecasts of the yield from snowmelt for periods of one to eight months beginning with March, and monthly and bi-monthly forecasts in winter

— since 1960, forecasts issued 2 to 5 times per week for the next three days, from October to March.

The forecast method is the least squares multiple regression, based on the observations of more than 50 years.

For the short term forecasts, the significant predictors are discharge and rainfall, eventually the snow cover percentage on the Swiss midland plateau. The largest errors of forecast were encountered in thawing conditions. Meteorological forecasts are provided by the Swiss Meteorological Institute.

For the long range forecasts, the alpine snow pack (in practice represented by the cumulative winter precipitations) and the levels of the Swiss lakes play an important role.

The forecasts are used by the hydro-power plants in their operation programmes for maintenance planning, and for proper timing of construction work.

In the future, the probability graduation of these forecasts will also be introduced in the optimisation of power production according to the methods of operations research.     

Development of a real-time forecasting model for turbidity current arrival time to improve reservoir desilting operation

ABSTRACT

Among various strategies for sediment reduction, venting turbidity currents through dam outlets can be an efficient way to reduce suspended sediment deposition. The accuracy of turbidity current arrival time forecasts is crucial for the operation of reservoir desiltation. A turbidity current arrival time (TCAT) model is proposed. A multi-objective genetic algorithm (MOGA), a support vector machine (SVM) and a two-stage forecasting technique are integrated to obtain more effective long lead-time forecasts of inflow discharge and inflow sediment concentration. The multi-objective genetic algorithm (MOGA) is applied for determining the optimal inputs of the forecasting model, support vector machine (SVM). The two-stage forecasting technique is implemented by adding the forecasted values to candidate inputs for improving the long lead-time forecasting. Then, the turbidity current arrival time from the inflow boundary to the reservoir outlet is calculated. To demonstrate the effectiveness of the TCAT model, it is applied to Shihmen Reservoir in northern Taiwan. The results confirm that the TCAT model forecasts are in good agreement with the observed data. The proposed TCAT model can provide useful information for reservoir sedimentation management during desilting operations.     

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM–LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.     

Shear wave splitting and polarization anomaly with deep seismic sounding data

In this paper we study the S wave basement velocity structure and S wave splitting and polarization anomalies using the three component records of deep seismic sounding in the Three Groges Dam area, and further discuss the methods to study the anisotropy of crustal medium, the direction of regional tectonic stress field, and the physical indicator of potential earthquake sources using shear wave splitting and polarization anomalies. It has been demonstrated by analyzing the DSS data in the Three Gorges Dam area that S wave splitting and polarization anomalies are reliable means for studying seismic anisotropy of crustal medium and the direction of regional tectonic stress field. At the same time, it has been shown that DSS data are very useful for widening S wave observing window and distinguishing anomalous body, because DSS data have the advantage that the source position is known prior and S wave ray path can be traced exactly. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,15, 1–3, 1993.     

Frontal dynamics boost primary production in the summer stratified Mediterranean sea

Bio-physical glider measurements from a unique process-oriented experiment in the Eastern Alboran Sea (AlborEx) allowed us to observe the distribution of the deep chlorophyll maximum (DCM) across an intense density front, with a resolution (～ 400 m) suitable for investigating sub-mesoscale dynamics. This front, at the interface between Atlantic and Mediterranean waters, had a sharp density gradient (Δρ ～ 1 kg/m³ in ～ 10 km) and showed imprints of (sub-)mesoscale phenomena on tracer distributions. Specifically, the chlorophyll-a concentration within the DCM showed a disrupted pattern along isopycnal surfaces, with patches bearing a relationship to the stratification (buoyancy frequency) at depths between 30 and 60 m. In order to estimate the primary production (PP) rate within the chlorophyll patches observed at the sub-surface, we applied the Morel and Andrè (J Geophys Res 96:685–698 1991) bio-optical model using the photosynthetic active radiation (PAR) from Argo profiles collected simultaneously with glider data. The highest production was located concurrently with domed isopycnals on the fresh side of the front, suggestive that (sub-)mesoscale upwelling is carrying phytoplankton patches from less to more illuminated levels, with a contemporaneous delivering of nutrients. Integrated estimations of PP (1.3 g C m^?2d^?1) along the glider path are two to four times larger than the estimations obtained from satellite-based algorithms, i.e., derived from the 8-day composite fields extracted over the glider trip path. Despite the differences in spatial and temporal sampling between instruments, the differences in PP estimations are mainly due to the inability of the satellite to measure DCM patches responsible for the high production. The deepest (depth > 60 m) chlorophyll patches are almost unproductive and probably transported passively (subducted) from upper productive layers. Finally, the relationship between primary production and oxygen is also investigated. The logarithm of the primary production in the DCM interior (chlorophyll (Chl) > 0.5 mg/m³) shows a linear negative relationship with the apparent oxygen utilization, confirming that high chlorophyll patches are productive. The slope of this relationship is different for Atlantic, mixed interface waters and Mediterranean waters, suggesting the presence of differences in planktonic communities (whether physiological, population, or community level should be object of further investigation) on the different sides of the front. In addition, the ratio of optical backscatter to Chl is high within the intermediate (mixed) waters, which is suggestive of large phytoplankton cells, and lower within the core of the Atlantic and Mediterranean waters. These observations highlight the relevance of fronts in triggering primary production at DCM level and shaping the characteristic patchiness of the pelagic domain. This gains further relevance considering the inadequacy of optical satellite sensors to observe DCM concentrations at such fine scales.     

Monitoring Etna volcanic plumes using a scanning LiDAR

In this paper, we use data obtained from LiDAR measurements during an ash emission event on 15 November 2010 at Mt. Etna, in Italy, in order to evaluate the spatial distribution of volcanic ash in the atmosphere. A scanning LiDAR system, located at 7?km distance from the summit craters, was directed toward the volcanic vents and moved in azimuth and elevation to analyse different volcanic plume sections. During the measurements, ash emission from the North East Crater and high degassing from the Bocca Nuova Crater were clearly visible. From our analysis we were able to: (1) evaluate the region affected by the volcanic plume presence; (2) distinguish volcanic plumes containing spherical aerosols from those having non-spherical ones; and (3) estimate the frequency of volcanic ash emissions. Moreover, the spatial distribution of ash mass concentration was evaluated with an uncertainty of about 50?%. We found that, even during ash emission episodes characterised by low intensity like the 15 November 2010 event, the region in proximity of the summit craters should be avoided by air traffic operations, the ash concentration being greater than 4?×?10^?3?g/m³. The use of a scanning permanent LiDAR station may usefully monitor the volcanic activity and help to drastically reduce the risks to aviation operations during the frequent Etna eruptions.     

Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba,Ecuador

Abstract

Reservoir operation is studied for the Daule Peripa and Baba system in Ecuador, where El Niño events cause anomalously heavy precipitation. Reservoir inflow is modelled by a Markov-switching model using El Niño–Southern Oscillation (ENSO) indices as input. Inflow is forecast using 9-month lead time ENSO forecasts. Monthly reservoir releases are optimized with a genetic algorithm, maximizing hydropower production during the forecast period and minimizing deviations from storage targets. The method is applied to the existing Daule Peripa Reservoir and to a planned system including the Baba Reservoir. Optimized operation is compared to historical management of Daule Peripa. Hypothetical management scenarios are used as the benchmark for the planned system, for which no operation policy is known. Upper bounds for operational performance are found via dynamic programming by assuming perfect knowledge of future inflow. The results highlight the advantages of combining inflow forecasts and storage targets in reservoir operation.

Editor D. Koutsoyiannis; Associate editor I. Nalbantis     

Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season

As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today’s optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf’s initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.     

Finite-difference migration of the field of refracted waves in studies of the deep structure of the Earth’s crust and the upper mantle based on the DSS (on the example of the <Emphasis Type="Italic">DOBRE</Emphasis> profile)

The main results of deep seismic sounding (DSS) are usually presented in the form of high-velocity models of the medium. Some model examples and the international DOBRE profile have shown that the informativeness of the data obtained can be significantly enhanced by the construction of wave images of the Earth’s crust, based on the migration of refracted and wide-angle reflected waves. The Donets Basin Refraction/Reflection Experiment (DOBRE) profile crosses the Dnieper-Donets paleorift zone in the Donbas region. Along the profile, refracted waves from the basement and the upper mantle and the reflections from the crust basement (the M boundary) are reliably traced. This wave migration has been used to construct a wave image of the structure of the Earth’s crust. As a result, a clear seismic image of the basement surface, whose depth changes along the profile from 0 to 20 km, was obtained. In near-slope parts of the basin, several major faults were identified that had not been identified previously during standard kinematic data processing. It is shown that the crust-upper mantle transition zone is a clearly reflective horizon only within the crystalline massifs; under a depression, it is represented by a lens-shaped highly-heterogeneous area. As shown in the model examples, the images obtained using such a migration accurately reflect the structural features of the medium, in spite of its complicated structure.     

Nonlinear analysis of seasonality and stochasticity of the Indus River

Abstract

One of the world's largest irrigation networks, based on the Indus River system in Pakistan, faces serious scarcity of water in one season and disastrous floods in another. The system is dominated both by monsoon and by snow and glacier dynamics, which confer strong seasonal and inter-annual variability. In this paper two different forecasting methods are utilized to analyse the long-term seasonal behaviour of the Indus River. The study also assesses whether the strong seasonal behaviour is dominated by the presence of low-dimensional nonlinear dynamics, or whether the periodic behaviour is simply immersed in random fluctuations. Forecasts obtained by nonlinear prediction (NLP) and the seasonal autoregressive integrated moving average (SARIMA) methods show that the performance of NLP is relatively better than the SARIMA method. This, along with the low values of the correlation dimension, is indicative of low-dimensional nonlinear behaviour of the hydrological dynamics. A relatively better performance of NLP, using an inverse technique, may also be indicative of the low-dimensional behaviour. Moreover, the embedding dimension of the best NLP forecasts is in good agreement with the estimated correlation dimension. This provides evidence that the nonlinearity inherent in the monthly river flow due to the snowmelt and the monsoon variations dominate over the high-dimensional components and might be exploited for prediction and modelling of the complex hydrological system.

Citation Hassan, S. A. & Ansari, M. R. K. (2010) Nonlinear analysis of seasonality and stochasticity of the Indus River. Hydrol. Sci. J. 55(2), 250–265.     

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

The major purpose of this study is to effectively construct artificial neural networks‐based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource‐derived forecasts (P_r), NWP precipitation forecasts (P_n), and improved precipitation forecasts (P_m) by merging P_r and P_n. The analysis shows that the accuracy of P_m is higher than that of P_r and P_n. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4–6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)‐based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1–6‐h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time‐lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1–3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4–6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling. Copyright © 2009 John Wiley & Sons, Ltd.     

The use of interplanetary scintillation maps in the prediction of geomagnetic activity

Interplanetary scintillation (IPS), the twinkling of small angular diameter radio sources, arises from the interaction of the signal with small-scale plasma irregularities in the solar wind. The technique may be used to sense remotely the near-Earth heliosphere and has potential for tracking large-scale interplanetary disturbances from close to the Sun to the Earth. Such observations might be useful within routine geomagnetic forecasts, and we use data from the Mullard Radio Astronomy Observatory to test this suggestion. A forecast was based on the visual evaluation of each daily map. If an IPS event was observed then we proposed that any associated geomagnetic activity would occur either on that day, or during the following two days. We consider the success of these forecasts in predicting days when either an SSC/SI or an Ap value exceeding 30 were recorded. The identification of IPS events is necessarily subjective and so two observers compiled independent events lists, and the results were compared. Approximately half of the IPS events in each list were followed by a geomagnetic signature but comparison of the two lists showed that different days were being chosen. We also found that the forecasts had very high false alarm rates. Since IPS is sensitive to a volume we did not expect all events to be associated with a geomagnetic signature. However, the technique failed to forecast a large proportion of geomagnetic events and the association between IPS events and geomagnetic activity is not much better than would be expected by chance. Comparing the IPS forecasts with forecasts of Ap released by the Space Environment Services Center (SESC) we found that SESC correctly predicted a similar proportion of days when Ap\geq30, but that the performance was significantly better than would be expected by chance, and had a much lower false alarm rate. We conclude that these IPS data cannot be used alone to produce reliable geomagnetic activity forecasts.