Dense Observation Network Research Articles

Shifting and extension of phenological periods with increasing temperature along elevational transects in southern Bavaria

The impact of global warming on phenology has been widely studied, and almost consistently advancing spring events have been reported. Especially in alpine regions, an extraordinary rapid warming has been observed in the last decades. However, little is known about phenological phases over the whole vegetation period at high elevations. We observed 12 phenological phases of seven tree species and measured air temperature at 42 sites along four transects of about 1000 m elevational range in the years 2010 and 2011 near Garmisch-Partenkirchen, Germany. Site- and species-specific onset dates for the phenological phases were determined and related to elevation, temperature lapse rates and site-specific temperature sums. Increasing temperatures induced advanced spring and delayed autumn phases, in which both yielded similar magnitudes. Delayed leaf senescence could therefore have been underestimated until now in extending the vegetation period. Not only the vegetation period, but also phenological periods extended with increasing temperature. Moreover, sensitivity to elevation and temperature strongly depends on the specific phenological phase. Differences between species and groups of species (deciduous, evergreen, high elevation) were found in onset dates, phenological response rates and also in the effect of chilling and forcing temperatures. Increased chilling days highly reduced forcing temperature requirements for deciduous trees, but less for evergreen trees. The problem of shifted species associations and phenological mismatches due to species-specific responses to increasing temperature is a recent topic in ecological research. Therefore, we consider our findings from this novel, dense observation network in an alpine area of particular importance to deepen knowledge on phenological responses to climate change.

A Hybrid MPI–OpenMP Parallel Algorithm and Performance Analysis for an Ensemble Square Root Filter Designed for Multiscale Observations

Abstract A hybrid parallel scheme for the ensemble square root filter (EnSRF) suitable for parallel assimilation of multiscale observations, including those from dense observational networks such as those of radar, is developed based on the domain decomposition strategy. The scheme handles internode communication through a message passing interface (MPI) and the communication within shared-memory nodes via Open Multiprocessing (OpenMP) threads. It also supports pure MPI and pure OpenMP modes. The parallel framework can accommodate high-volume remote-sensed radar (or satellite) observations as well as conventional observations that usually have larger covariance localization radii. The performance of the parallel algorithm has been tested with simulated and real radar data. The parallel program shows good scalability in pure MPI and hybrid MPI–OpenMP modes, while pure OpenMP runs exhibit limited scalability on a symmetric shared-memory system. It is found that in MPI mode, better parallel performance is achieved with domain decomposition configurations in which the leading dimension of the state variable arrays is larger, because this configuration allows for more efficient memory access. Given a fixed amount of computing resources, the hybrid parallel mode is preferred to pure MPI mode on supercomputers with nodes containing shared-memory cores. The overall performance is also affected by factors such as the cache size, memory bandwidth, and the networking topology. Tests with a real data case with a large number of radars confirm that the parallel data assimilation can be done on a multicore supercomputer with a significant speedup compared to the serial data assimilation algorithm.

Innovations and applications of the VERA quality control

Abstract. Quality control (QC) is seen today as an important scientific field to increase the value of observational data. Whereas most QC methods are linked to atmospheric modeling (being part of the data assimilation procedure), in this paper the focus is on the application of a model independent QC method based on data self consistency recently published: VERA-QC. A special challenge is the QC of data in complex terrain which requires special treatment in terms of data selection and data transformation. In this context, some special VERA-QC modules such as the consideration of significant elevation differences of adjacent stations or the consideration of transformed temperature values will be discussed. The system detects gross errors as well as biases and offers objective correction proposals (deviations) for each observation. The essential gross error detection is not only based on the statistical behavior of station specific deviations, but also on the rate of cost function reduction. Beside a two dimensional application, higher dimensionalities may also be chosen, for instance including the time coordinate. Applications and results are discussed for pressure, temperature as well as for precipitation data which needs, however, a very dense observation network. Real time application of VERA-QC allows the production of high quality fields of meteorological parameters, which can be used, e.g. for nowcasting as well as for model unbiased validation of prognostic models.

A Simple Groundwater Scheme for Hydrological and Climate Applications: Description and Offline Evaluation over France

Abstract Despite their potential influences on surface water and climate, groundwater processes are generally not represented in climate models. Here, a simple groundwater scheme including two-dimensional flow dynamics and accounting for groundwater–river exchanges is introduced into the global Total Runoff Integrated Pathways (TRIP) river routing model coupled to the Météo-France climate model. This original scheme is tested in offline mode over France at high () and low (0.5°) resolution against a dense network of river discharge and water table observations over the 1970–2010 period, and is compared to the fine-tuned Système d’Analyze Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)–Interactions between Soil, Biosphere, and Atmosphere (ISBA) coupled hydrometeorological model (MODCOU). In addition, the simulated terrestrial water storage (TWS) variations are compared to the TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The aquifer basins over France are defined using the World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP) groundwater resources map, a simplified French lithological map, and the International Geological Map of Europe (IGME). TRIP is forced by daily runoff and drainage data derived from a preexisting simulation of the ISBA land surface scheme driven by the high-resolution SAFRAN meteorological analysis. Four simulations are carried out with or without groundwater at both resolutions. Results show that the groundwater scheme allows TRIP to better capture the spatiotemporal variability of the observed river discharges and piezometric heads. Summer base flows are particularly improved over the main rivers of France. Decreasing the horizontal resolution has a limited impact on the simulated discharges, while it slightly degrades the simulation of water table variations.

Characterizing Curvilinear Features Using the Localized Normal‐Score Ensemble Kalman Filter

The localized normal‐score ensemble Kalman filter is shown to work for the characterization of non‐multi‐Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady‐state observation data are not sufficient to identify the conductivity channels. Transient‐state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observation data, and it deteriorates as the number of observation piezometers decreases. It is also remarkable that, even when the prior model structure is wrong, the localized normal‐score ensemble Kalman filter can produce acceptable results for a sufficiently dense observation network.

Hydrological ensemble forecasting in mesoscale catchments: Sensitivity to initial conditions and value of reforecasts

Hydrological forecasts can benefit from model climatologies compiled from long, consistent, retrospective forecasts (reforecasts). Typical areas of application include the estimation of return periods for extreme events, the detection of model deficiencies, such as systematic errors or the calibration of current forecasts using the reforecasts, and suitable observations. One difficulty when creating long reforecasts is the availability of good states for the model initialization, as a sufficiently dense network of meteorological observations is rarely available in most catchments for a long period. With this study, the creation of forecast and reforecasts in such catchments is motivated by considering two aspects: first, a comparison where it is shown that hydrological forecasts benefit most from high‐quality initial conditions on the basis of local observations, however, less accurate initial conditions using ERA‐interim reanalysis are also sufficient to provide skillful hydrological forecasts useful for many users. Second, we demonstrate that hydrological reforecasts compiled without long‐term meteorological observations have an additional value, e.g., they allow for more skillful runoff forecasts. Utilizing those reforecasts can compensate for forecast errors induced by less accurate initializations. For this study, hourly hydrological reforecasts, based on the PREcipitation‐Runoff‐EVApotranspiration HRU Model (PREVAH) were used, with an 18 year long, five‐member global ensemble reforecast data set from the European Centre for Medium Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) as forcing for lead times up to 10 days. Three mesoscale catchments of different characteristics in the Swiss Alps were analyzed.

Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake

[1] The 2011 March 11 Tohoku-Oki earthquake (Mw9.0) caused vast damages to the country. Large events beneath dense observation networks could bring breakthroughs to seismology and geodynamics, and here I report one such finding. The Japanese dense network of Global Positioning System (GPS) detected clear precursory positive anomaly of ionospheric total electron content (TEC) around the focal region. It started ∼40 minutes before the earthquake and reached nearly ten percent of the background TEC. It lasted until atmospheric waves arrived at the ionosphere. Similar preseismic TEC anomalies, with amplitudes dependent on magnitudes, were seen in the 2010 Chile earthquake (Mw8.8), and possibly in the 2004 Sumatra-Andaman (Mw9.2) and the 1994 Hokkaido-Toho-Oki (Mw8.3) earthquakes, but not in smaller earthquakes.

Optimising an observational water monitoring network for Archipelago Sea, South West Finland

Water quality monitoring in topographically fragmented archipelago coasts calls for a dense observational network. However, visiting multiple sites and analyzing the samples requires a significant amount of work, leading to considerable economic cost. It is of interest to determine an efficient set of sites, which still offers adequate information on the water quality with a sufficient spatial accuracy. A method for optimizing an existing observational network is proposed. The method is concretized by applying it for an observational network in the Archipelago Sea, South West Finland. The network is pruned with the requirement that the observations of the removed sites can be estimated using those of the remaining sites. Suboptimal heuristics are used in pruning to keep the computational time acceptable. Some observations are not available and need to be estimated (imputed) before the pruning. For the network in the Archipelago Sea, the results of the pruning are somewhat sensitive to differences in imputed datasets and heuristics used for site selection.

An Empirical Latent Heat Flux Parameterization for the Noah Land Surface Model

Abstract Proper partitioning of the surface energy fluxes that drive the evolution of the planetary boundary layer in numerical weather prediction models requires an accurate representation of initial land surface conditions. Unfortunately, soil temperature and moisture observations are unavailable in most areas and routine daily estimates of vegetation coverage and biomass are not easily available. This gap in observational capabilities seriously hampers the evaluation and improvement of land surface parameterizations, since model errors likely relate to improper initial conditions as much as to inaccuracies in the parameterizations. Two unique datasets help to overcome these difficulties. First, 1-km fractional vegetation coverage and leaf area index values can be derived from biweekly maximum normalized difference vegetation index composites obtained from daily observations by the Advanced Very High Resolution Radiometer onboard NOAA satellites. Second, the Oklahoma Mesonet supplies multiple soil temperature and moisture measurements at various soil depths each hour. Combined, these two datasets provide significantly improved initial conditions for a land surface model and allow an evaluation of the accuracy of the land surface model with much greater confidence than previously. Forecasts that both include and neglect these unique land surface observations are used to evaluate the value of these two data sources to land surface initializations. The dense network of surface observations afforded by the Oklahoma Mesonet, including surface flux data derived from special sensors, provides verification of the model results, which indicate that predicted latent heat fluxes still differ from observations by as much as 150 W m−2. This result provides a springboard for assessing parameterization errors within the model. A new empirical parameterization developed using principal-component regression reveals simple relationships between latent heat flux and other surface observations. Periods of very dry conditions observed across Oklahoma are used advantageously to derive a parameterization for evaporation from bare soil. Combining this parameterization with an empirical canopy transpiration scheme yields improved sensible and latent heat flux forecasts and better partitioning of the surface energy budget. Surface temperature and mixing ratio forecasts show improvement when compared with observations.

Long-term geomagnetic changes observed n association with earthquake swarm activities in the Izu Peninsula, Japan

Anomalous crustal uplift has continued since 1976 in the Izu Peninsula, Japan. Earthquake swarms have also occurred intermittently off the coast of Ito since 1978. Observations of the total intensity of the geomagnetic field in the peninsula started in 1976 to detect anomalous changes in association with those crustal activities. In particular, a dense continuous observation network using proton magnetometers was established in the northeastern part of the peninsula, immediately after the sea-floor eruption off the coast of Ito in 1989. No remarkable swarm activities were observed there from 1990 to 1992. However, after the occurrence of a small swarm in January 1993, five large swarm activities were observed. At some observation sites, we observed a remarkable long-term trend in the total geomagnetic field in association with the change in the distribution pattern in the seismicity of the earthquake swarms.

Supplement to The Tropical Warm Pool International Cloud Experiment

One of the most comprehensive data sets of tropical cloud systems and their environmental setting and impacts ever sampled has been collected during the Tropical Warm Pool International Cloud Experiment in the area around Darwin, Northern Australia in January and February of 2006. The experiment design utilized permanent observational facilities in Darwin which include a polarimetric weather radar operated by the Australia Bureau of Meteorology (BOM) and a suite of cloud remote sensing instruments operated by the DOE Atmospheric Radiation Measurement (ARM) program. A dense network of observations added for the experiment included ocean observations and a dense balloon-borne sounding network. An integral factor in the design was to provide boundary conditions and validation data sets for a range of modelling activities and cloud retrieval development. A fleet of five research aircraft were deployed including two high altitude aircraft for characterizing cloud properties and the atmospheric state, a plane carrying airborne cloud radar and lidar and two aircraft sampling the boundary layer in great detail including fluxes, aerosols and chemistry.

Climate variability in north-western Italy during the second half of the 20th century

We analyse a large set of daily temperature and precipitation time series measured by a dense observational network in north-western Italy, in the period from 1952 to 2002. We find that average temperatures display a significant increase of about 1 °C over the period of observation. The increase is particularly evident for maximum daily temperatures in winter and summer months. By contrast, precipitation time series display no significant trend over the last fifty years. We also determine the statistical properties of interannual fluctuations in temperature and precipitation, and quantify their correlation with large-scale atmospheric patterns and global indices such as the North Atlantic Oscillation (NAO), the Scandinavian pattern (SCAN) and European Blocking (EB). The positive phase of the Scandinavian pattern and the presence of frequent blocking episodes are found to be significantly correlated with increased summer and fall precipitation and cold temperatures in the study area.

Seasonally dependent changes of precipitation extremes over Germany since 1950 from a very dense observational network

The newly updated collection of daily precipitation measurements over Western Germany (more than 2000 stations in total) is used to analyze linear trends in extreme and heavy precipitation for different seasons over the period 1950–2004. Heavy and extreme precipitation has been quantified using the 95% and 99% percentiles with respect to the Gamma distribution fitted to daily precipitation data. The significance of linear trends was quantified using several statistical tests including estimates of field significance. Positive linear tendencies in heavy precipitation for the winter, spring and autumn seasons were found for the whole domain with the largest increase of 13% per decade in Central and Southern Germany. For the summer season, however, heavy precipitation exhibits mostly negative trends of up to 8% per decade e.g., for the Central and Southwestern parts of Germany. Trends derived from the estimates of heavy precipitation without seasonal breakdown, however, do not show any clear spatial pattern. Estimates of field significance show that the conclusions concerning the seasonal diversity in trend sign hold for most of Western Germany. The results are insensitive to changes of the beginning and the end of the records by several years; thus the seasonal linear trend patterns are not influenced by interdecadal variability. Seasonality is also identified in the linear trends of mean precipitation characteristics. Analysis performed for different classes of precipitation intensity shows that during winter the linear increase of heavy and extreme precipitation is associated with downward linear tendencies for weak precipitation. In summer statistically significant negative linear trends were identified for all classes of precipitation intensities. Our results also imply that the amplitude of the annual cycle of heavy and extreme precipitation underwent a considerable decrease during the last 55 years between 30% to 60% per decade.

Daily precipitation observations over Emilia‐Romagna: mean values and extremes

AbstractDaily precipitation data from a dense observational network covering Emilia‐Romagna, a region of Northern Italy, are described and analysed. Data are available for all stations for the period 1951–2004 and for a selected group of stations located over the Reno hydrological basin for the period 1925–2004. Indices describing seasonal values of mean precipitation and frequency of extreme events are computed starting from daily data and are used to describe the temporal and spatial variability of precipitation over the region.Data referring to the period 1951–2004 are used to describe trends of relevant precipitation indices over the same period in all seasons, and the relation between the variability of these indices and major Euro‐Atlantic large‐scale circulation indices over winter.Data referring to the period 1925–2004 are used to analyse the decadal and long‐term variability over the Reno Basin and its relation with the winter daily discharge of the river. This analysis allows identifying the presence of a clear decadal periodicity in river discharge, strongly related with the decadal variability in both total precipitation and frequency of intense events. Copyright © 2008 Royal Meteorological Society

수자원 영향평가에 활용 가능한 지역기후변화 시나리오 연구

온실가스 증가로 인한 기후변화를 이해하고, 다양한 영향평가 분야에 상세한 기후정보를 제공하기 위해서 온실가스 배출 시나리오에 근거한 지역기후변화 시나리오 연구가 수행되었다. 본 연구에서는 역학적 상세화를 위하여 지역기후모델을 이용한 이중둥지격자시스템을 구축하고, 과거 30년(1971-2000)과 미래 30년(2021-2050)에 대한 시나리오를 생산하였다. 미래 시나리오에 대한 신뢰도를 확보하기 위하여, 기준 시나리오에 대한 관측과의 검증이 선행되었다. 기준 시나리오는 둥지격자의 한반도 영역뿐만 아니라 모격자의 동아시아 영역에 대하여 기온과 강수의 계절 및 경년변동성과 일빈도분포를 성공적으로 모사하였다. 또한 경계조건으로 이용된 전구모델과 비교하여 공간적인 특성뿐만 아니라 면적평균 시계열에서도 뚜렷한 오차의 감소를 나타내었다. 미래 기후변화 전망은 기준 시나리오와 미래 시나리오의 차이로부터 유도되며, 평균적인 변화뿐만 아니라 극한 기후의 빈도와 강도변화에 대한 분석이 수행되었다. 미래 시나리오에 의하면 2050년까지 한반도에서는 <TEX>$2^{\circ}C$</TEX> 정도의 기온 상승과 겨울철 강수량의 뚜렷한 증가경향이 전망되었다. 본 연구는 한반도의 복잡한 지형적 특성이 반영된 고해상도 시나리오를 생산하기 위한 방법론을 제시하고, 생산된 시나리오를 이용하여 다양한 시공간 규모에 대한 기후특성을 파악하였다는데 의미가 있다. Our ultimate purpose is to investigate the potential change in regional surface climate due to the global warming and to produce higher quality regional surface climate information over the Korean peninsula for comprehensive impact assessment. Toward this purpose, we carried out two 30-year long experiments, one for present day conditions (covering the period 1971-2000) and one for near future climate conditions (covering the period 2021-2050) with a regional climate model (RegCM3) using a one-way double-nested system. In order to obtain the confidence in a future climate projection, we first verify the model basic performance of how the reference simulation is realistic in comparison with a fairly dense observation network. We then examine the possible future changes in mean climate state as well as in the frequency and intensity of extreme climate events to be derived by difference between climate condition as a baseline and future simulated climate states with increased greenhouse gas. Emphasis in this study is placed on the high-resolution spatial/temporal aspects of the climate change scenarios under different climate settings over Korea generated by complex topography and coastlines that are relevant on a regional scale.

Supporting large-scale hydrogeological monitoring and modelling by time-variable gravity data

One of the key variables for hydrogeological monitoring and modelling studies are temporal variations of the water storage in groundwater systems. Storage variations are a fundamental component of the groundwater balance and of the continental water cycle. Assessing groundwater storage change is central to water management with regard to water resources, ecology (e.g., wetland preservation), or engineering (e.g., land subsidence due to groundwater withdrawal). In particular, at large spatial scales, however, measuring groundwater storage change is demanding. A dense observation network of wells is required to obtain reliable area-average values. For large aquifers or river basins and for remote areas this approach may not be feasible. An alternative method that has rarely been applied in hydrogeology is to monitor mass changes that are associated with water storage variations by means of gravity surveys. Measurements with superconducting gravimeters have shown that variations in the groundwater level or other hydrological features near the monitoring station may have a significant impact on the observed time-variable gravity signal (Kroner and Jahr 2006) and allow for the determination of aquifer storage changes at the local scale (Pool 2005). At the global scale, gravity satellite missions make use of the basic principle that the satellite’s motion around the Earth is dominated by the Earth’s gravity field. Thus, tracking perturbations of the satellite orbit allows for the determination of the underlying spatial and temporal variations of the gravity field. These very small perturbations originate, on the one hand, from the spatially inhomogeneous but quasi-static mass distribution of the solid Earth and, on the other hand, from the even smaller temporal variations caused by mass fluxes in the vicinity of the Earth’s surface by the atmosphere, oceans, and hydrosphere. The breakthrough with respect to hydrological applications, as outlined in the fundamental contributions byWahr et al. (1998) and Dickey et al. (1999), came with the GRACE. The GRACE (Gravity Recovery and Climate Experiment) satellite mission was launched by NASA (National Aeronautics and Space Administration) and DLR (German Aerospace Center) in March 2002 (Tapley et al. 2004a). The objective of GRACE is to map the Earth’s gravity field every month with a spatial resolution of a few hundred km. The mission consists of two identical satellites co-orbiting in the same, almost polar, orbital plane at a distance of approximately 220 km from each other along their track and at an initial altitude of about 500 km. The key element is a micrometer precise satellite link continuously measuring the relative distance of the satellites from each other, which is highly sensitive to the variations of the gravity field. While the intended lifetime of GRACE was 5 years, a considerably longer lifetime reaching until about 2016 can be expected based on the actual mission status (according to an internal report by GFZ Potsdam (Germany’s National Research Centre for Geosciences, 2006, unpublished data). Recent results mark impressive progress in the determination of mass variations relevant for large-scale hydrological and hydrogeological purposes from GRACE time-variable gravity fields. Several studies inferred water storage change on the continents (e.g., Andersen et al. 2005; Ramillien et al. 2005; Schmidt et al. 2006b; Swenson and Milly 2006; Tapley et al. 2004b; Wahr et al. 2004). The results clearly show seasonal and inter-annual changes in water storage at the scale of continents and large river basins that roughly correspond to simulation results of global hydrological and climate models. Discrepancies in amplitude and phase help to identify model limitations such as in model structure, process description or parameterization, and errors in model input data such as climate data. On the other hand, several error components are added to GRACE data during progression from raw satellite data to the final hydrological product: (1) GRACE measurement errors (such as instrument or orbit errors), (2) errors in the background data used for reducing the total GRACE mass signal to (ground) water variations, and (3) leakage of signals from outside a selected region of interest.

Experiment to characterize tropical cloud systems

A major experiment to study tropical convective cloud systems and their impacts will take place around Darwin, northern Australia, in January and February 2006. The Tropical Warm Pool International Cloud Experiment (TWP‐ICE) aims to study cloud systems and their radiative, chemical, and moisture budget impacts from the initial convection through to the decaying, convectively generated cirrus.This broad aim is being addressed by a dense observational network that includes two sites equipped with cloud radar and lidar, a polarimetric (dual‐polarization) weather radar and another operational Doppler radar in the area, wind profilers, infrared interferometers, flux stations, lightning interferometers, and oceanographic measurements. Also, an array of five radiosonde sites arranged in a ring with a diameter of about 250 km around Darwin will conduct soundings every three hours for a period of 24 days.

Comments on “The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis”

The paper by Riishojgaard et al. (2004) investigates the assimilation and impact of prospective Doppler wind lidar (DWL) line-of-sight (LOS) single-perspective winds in meteorological analysis. It is argued that single-component wind observations are far less effective in reducing wind analysis error than vector wind information. This work has relevance because the prospects are good that space-based DWL instruments will provide accurate wind profiles of single-perspective LOS wind profile measurements in the future. Riishojgaard et al. rightly argue that the usefulness of such winds needs to be well addressed in the design phase of space missions. The forthcoming European Space Agency Atmospheric Dynamics Mission (ADM), called Aeolus, is referred to in this context. The Riishojgaard et al. study is carried out in an idealized and very simplified framework. Our concerns are 1) that the simple framework poorly represents the characteristics of a state-of-the-art global data assimilation system for numerical weather prediction (NWP) and 2) that the DWL scenarios that are discussed have abundant and unrealistic coverage and quality. As such, their conclusions may be misleading for, and contribute little toward, the critical design considerations for an affordable space-based DWL. The results (and the quality of the analyzed wind fields) could be far more realistic and, in our view, far more favorable for LOS winds in a more carefully designed experiment. The NWP analysis problem would be severely underdetermined if it were based on the observations alone. To overcome this problem, data assimilation typically combines the information provided by the relatively sparse observations with a short-range forecast on a dense grid (Daley 1991). Because the NWP model state is poorly observed, it is critical that local observation increments are carefully distributed spatially in a wider area. This process is done based on statistical knowledge of the background error structures. In a fourdimensional variational data assimilation (4DVAR) analysis system, information on the temporal evolution of the model state is also exploited. Around any local observation, information on the multivariate spatial correlation of the background errors, as represented in the background-error covariance matrix B, is used to provide a spatially coherent update of the model atmospheric state. For LOS wind analysis, the B covariance structures are crucial in both spatially interpolating the observed wind component and inferring the spatial pattern of the unobserved component of wind as well as the associated temperature and pressure increments. The design of the B matrix and the sampling strategy of the DWL space mission are the two most important factors that determine the impact of the data, both in real application and within the simplified framework of Riishojgaard et al. In the case in which B is poor, this would generally result in spatially poor analyses, especially when the observations are sparse or when one or several analysis variables are unobserved. In a relatively dense observation network, on the other hand, the multivariate spatial structures associated with many observations will overlap and the effect of an imperfect B will diminish (by oversampling). Our specific comments are in two areas. The first is that the Riishojgaard et al. paper uses a synthetic vortex Corresponding author address: Dr. Ad Stoffelen, Royal Netherlands Meteorological Institute, Postbus 201, 3730 AE de Bilt, Netherlands. E-mail: ad.stoffelen@knmi.nl 1276 J O U R N A L O F A P P L I E D M E T E O R O L O G Y VOLUME 44

Ensemble single column modeling (ESCM) in the tropical western Pacific: Forcing data sets and uncertainty analysis

Single column models (SCMs) are useful tools for the evaluation of parameterisations of radiative and moist processes used in general circulation models (GCMs). Most SCM studies to date have concentrated on regions where there is a sufficiently dense observational network to derive the required forcing data. This paper describes an ensemble single column modeling (ESCM) approach where the forcing data are derived from numerical weather prediction (NWP) analysis products. To highlight the benefits of the ESCM approach, four forcing data sets were derived for a two year period at the Tropical Western Pacific ARM (Atmospheric Radiation Measurement Program) sites at Manus Island and Nauru. In the first section of the study, the NWP derived forcing data are validated against a range of observations at the tropical sites. In the second section, the sensitivity of two different SCMs to uncertainties in the forcing data sets are analysed. It is shown that despite the inherent uncertainties in the NWP derived forcing data, an ESCM approach is able to identify errors in the SCM physics. This suggests the ESCM approach is useful for testing parameterisations in relatively observation sparse regions, such as the TWP.

The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification

Abstract. The limited-area ensemble prediction system COSMO-LEPS has been running every day at ECMWF since November 2002. A number of runs of the non-hydrostatic limited-area model Lokal Modell (LM) are available every day, nested on members of the ECMWF global ensemble. The limited-area ensemble forecasts range up to 120h and LM-based probabilistic products are disseminated to several national and regional weather services. Some changes of the operational suite have recently been made, on the basis of the results of a statistical analysis of the methodology. The analysis is presented in this paper, showing the benefit of increasing the number of ensemble members. The system has been designed to have a probabilistic support at the mesoscale, focusing the attention on extreme precipitation events. In this paper, the performance of COSMO-LEPS in forecasting precipitation is presented. An objective verification in terms of probabilistic indices is made, using a dense network of observations covering a part of the COSMO domain. The system is compared with ECMWF EPS, showing an improvement of the limited-area high-resolution system with respect to the global ensemble system in the forecast of high precipitation values. The impact of the use of different schemes for the parametrisation of the convection in the limited-area model is also assessed, showing that this have a minor impact with respect to run the model with different initial and boundary condition.