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A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles and unstaffed aerial vehicles, is to ensure their proper functioning under adverse rain or snow conditions. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution were recorded at General Motors’s McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer andWXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of the University of Ontario Institute of Technology. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data. [ABSTRACT FROM AUTHOR]

Since the mid-2000s, the Argo oceanographic observational network has provided near-real-time fourdimensional data for the global ocean for the first time in history. Internet (i.e., the ''web'') applications that handle the more than two million Argo profiles of ocean temperature, salinity, and pressure are an active area of development. This paper introduces a new and efficient interactive Argo data visualization and delivery web application named Argovis that is built on a classic three-tier design consisting of a front end, back end, and database. Together these components allow users to navigate 4D data on a world map of Argo floats, with the option to select a custom region, depth range, and time period. Argovis's back end sends data to users in a simple format, and the front end quickly renders web-quality figures. More advanced applications query Argovis from other programming environments, such as Python, R, andMATLAB. Our Argovis architecture allows expert data users to build their own functionality for specific applications, such as the creation of spatially gridded data for a given time and advanced time-frequency analysis for a space-time selection. Argovis is aimed to both scientists and the public, with tutorials and examples available on the website, describing how to use the Argovis data delivery system--for example, how to plot profiles in a region over time or to monitor profile metadata. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using available observations through data assimilation could help to increase the accuracy of forecasts and provide reliable advisory information. This paper focuses on the use of satellite total-ash-column data in 4D-Var based assimilations. Experiments show that it is very difficult to estimate the vertical distribution of effective volcanic ash injection rates from satellite-observed ash columns using a standard 4D-Var assimilation approach. This paper addresses the ill-posed nature of the assimilation problem from the perspective of a spurious relationship. To reduce the influence of a spurious relationship created by a radiate observation operator, an adjoint-free trajectory-based 4D-Var assimilation method is proposed, which is more accurate to estimate the vertical profile of volcanic ash from volcanic eruptions. The method seeks the optimal vertical distribution of emission rates of a reformulated cost function that computes the total difference between simulated and observed ash columns. A 3D simplified aerosol transport model and synthetic satellite observations are used to compare the results of both the standard method and the new method. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) imaging probes, such as the 2D stereo (2D-S) probe and the cloud imaging probe (CIP), are routinely used to provide in situ measurements of cloud particle properties. The basic measurement is shadowgraphs of water drops and ice particles from which particle size distributions, projected particle area, and mass concentrations are determined. These data permeate data archives of domestic and foreign government agencies, universities, and the private sector. This paper provides results from laboratory tests and flight tests on a Learjet research aircraft that give new insights into the performance of the 2D imaging probes, and how their performance may have impacted measurements collected in data archives. The laboratory tests are conducted with the aid of two devices: 1) a droplet generator that provides known concentrations of water drops from 15 to 65 µm ± 1 µm that can be positioned in the probe's sample volume with 10-µm precision; and 2) a motorized spinning platform that supports transparent disks with small opaque features (i.e., a ''spinning disk''), which replicates the effect of particles transecting the probe's sample volume at translational speeds up to 190 ms-1. The flight tests were conducted with a Learjet research aircraft that collected cloud particle data at true airspeeds from 99 to 170 ms-1. The results provide new insights into how probe optics, time response, and data throughput of the 2D-S and CIP electro-optics impact the measurements of cloud particles. The results, summarized in the conclusions, suggest how archived data are impacted. [ABSTRACT FROM AUTHOR]

A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification. [ABSTRACT FROM AUTHOR]

This paper aims at generating a set of enriched analyses by assimilating the data collected during the South American Low-Level Jet Experiment (SALLJEX) in southeastern South America during the summer season 2002/03. The analyses are generated with the Brazilian Regional Atmospheric Modeling System (BRAMS) using all available observations and achieving 20-km horizontal resolution and 3-h temporal resolution. This paper describes the methods used to generate and evaluate the analyses. The results indicate that the method is appropriate because data assimilation provides an improved fit between analyses and observations. In addition, enriched analyses show a better representation of mesoscale structure in case studies. This satisfactory evaluation allows one to use the set of analyses to perform case studies at higher resolution than is currently available, with the certainty that these analyses reproduce the atmospheric conditions better than those that do not assimilate data. [ABSTRACT FROM AUTHOR]

The control variable transform (CVT) is a keystone of variational data assimilation. In publications using such a technique, the background term of the transformed cost function is defined as a canonical inner product of the transformed control variable with itself. However, it is shown in this paper that this practical definition of the cost function is not correct if the CVT uses a square root of the background error covariance matrix that is not square. Fortunately, it is then shown that there is a manifold of the control space for which this flaw has no impact, and that most minimizers used in practice precisely work in this manifold. It is also shown that both correct and practical transformed cost functions have the same minimum. This explains more rigorously why the CVT is working in practice. The case of a singular is finally detailed, showing that the practical cost function still reaches the best linear unbiased estimate (BLUE). [ABSTRACT FROM AUTHOR]

Localization and hybridization are two methods used in ensemble data assimilation to improve the accuracy of sample covariances. It is shown in this paper that it is beneficial to consider them jointly in the framework of linear filtering of sample covariances. Following previous work on localization, an objective method is provided to optimize both localization and hybridization coefficients simultaneously. Theoretical and experimental evidence shows that if optimal weights are used, localized-hybridized sample covariances are always more accurate than their localized-only counterparts, whatever the static covariance matrix specified for the hybridization. Experimental results obtained using a 1000-member ensemble as a reference show that the method developed in this paper can efficiently provide localization and hybridization coefficients consistent with the variable, vertical level, and ensemble size. Spatially heterogeneous optimization is shown to improve the accuracy of the filtered covariances, and consideration of both vertical and horizontal covariances is proven to have an impact on the hybridization coefficients. [ABSTRACT FROM AUTHOR]

The Wire Flyer towed vehicle is a new platform able to collect high-resolution water column sections. The vehicle is motivated by a desire to effectively capture spatial structures at the submesoscale. The vehicle fills a niche that is not achieved by other existing towed and repeat profiling systems. The Wire Flyer profiles up and down along a ship-towed cable autonomously using controllable wings for propulsion. At ship speeds between 2 and 5 kt (1.02–2.55 m s−1), the vehicle is able to profile over prescribed depth bands down to 1000 m. The vehicle carries sensors for conductivity, temperature, depth, oxygen, turbidity, chlorophyll, pH, and oxidation reduction potential. During normal operations the vehicle is typically commanded to cover vertical regions between 300 and 400 m in height with profiles that repeat at kilometer spacing. The vertical profiling speed can be user specified up to 150 m min−1. The high-density sampling capability at depths below the upper few hundred meters makes the vehicle distinct from other systems. During operations an acoustic modem is used to communicate with the vehicle to provide status information, data samples, and the ability to modify the sampling pattern. This paper provides an overview of the vehicle system, describes its operation, and presents results from several cruises. [ABSTRACT FROM AUTHOR]

Researchers are producing an ever greater number of web-based climate data and analysis tools in support of natural resource research and management. Yet the apparent absence or underreporting of evaluation in the development of these applications has raised questions as to whether, by whom, and for what they are utilized, and, relatedly, whether they meet the rationale for their development. This paper joins recent efforts to address these questions by introducing one approach to evaluation—developmental evaluation—and reporting on its use in the evaluation of the Southwest Climate and Environmental Information Collaborative (SCENIC). A web interface under development at the Western Regional Climate Center, SCENIC provides access to climate data and analysis tools to environmental scientists in support of natural resource research and management in the southwestern United States. Evaluation findings highlight subtlety in the improvements necessary for ensuring a useful and usable application that could not have been ascertained in the absence of end-user feedback. We therefore urge researchers to systematically evaluate web-based climate data and analysis tools in the interest of ensuring their usefulness, usability, and fulfillment of the proposed rationale. In so doing, we recommend that researchers test and apply established evaluation frameworks, thereby engaging end users directly in the process of application development. [ABSTRACT FROM AUTHOR]

First-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m. [ABSTRACT FROM AUTHOR]

This paper presents a method for determining a threshold value of probabilistic convective weather forecast data. By synchronizing air traffic data and an experimental probabilistic convective weather forecast product, it was observed that aircraft avoid areas of specific forecasted probability. Both intensity and echo top of the forecasted weather were synchronized with air traffic data to derive the probability threshold parameter. This value can be used by dispatchers for flight planning and by air traffic managers to reroute streams of aircraft around convective cells. The main contribution of this paper is to provide a method to compute the probability threshold parameters using a specific experimental probabilistic convective forecast product providing hourly guidance up to 6 h. Air traffic and weather data for a 4-month period during the summer of 2007 were used to compute the parameters for the continental United States. The results are shown for different altitudes, times of day, aircraft types, and airspace users. Threshold values for each of the 20 Air Route Traffic Control Centers were also computed. Additional details are presented for seven high-altitude sectors in the Fort Worth, Texas, center. For the analysis reported here, flight intent was not considered and no assessment of flight deviation was conducted since only aircraft tracks were used. [ABSTRACT FROM AUTHOR]

This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of high-resolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple two-layer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed. [ABSTRACT FROM AUTHOR]

The adjoint-based observation impact estimation method has been providing essential information to improve data assimilation systems (DASs) in numerical weather prediction (NWP). This paper has two purposes. The first is to verify the four approximations used in the method: iterative construction of the Kalman gain adjoint operator, the tangent linear (TL) approximation of a forecast model, ignoring cross terms of observation impacts, and approximations for incremental DASs. The second is to add new information to our knowledge of observation impacts. For the verification of the adjoint-based method, we use the TL-based observation impact estimation method that can calculate the same quantity as the adjoint-based method without adjoint calculations. Results of these verifications and observation impact estimations performed on the global NWP system of the Japan Meteorological Agency are as follows. First, observation impacts calculated using the adjoint-based method agree well with those from the TL-based method, with the correlation coefficient between them exceeding 0.97. Second, estimated observation impacts are consistent with previous studies in many aspects. There are also system-dependent properties, such as relatively small impacts from GPS radio occultation data above 13 km. Furthermore, new aspects of observation impacts are found: 1) the probability density function of the observation impact agrees well with the scalar theory when giving the experimental value of the observation and the forecast error standard deviations; 2) later observations in the data assimilation window have larger positive impacts; and 3) impacts of AMSU-A in the Southern Hemisphere are more than double those in the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

This paper describes the repackaging and consistent distribution of the world's major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use. [ABSTRACT FROM AUTHOR]

Inference about time series in weather data can be made in several ways. Current practice focuses on computing summary measures, such as mean and variance, or constructing a reference year from small subsets of data derived from multiple years. Some applications require the selection of an instance of observed data over a fixed time frame, typically a year or more, for modeling. In addition, many current methods do not include rainfall as a parameter of interest. This paper reviews and refines existing methods for determining a reference year by creating a metric that measures the (abstract) distance between observed patterns of rainfall. The reference year is then chosen from a group of potential reference years. This method is computationally efficient, easily explained, and robust against differences in the index of reporting, to include leap years. Application of the distance metric to data from Philadelphia, Pennsylvania, and Norfolk, Virginia, shows that it appropriately identifies not only the years that are most typical for a location, but extreme years as well. Both are particularly useful in applications related to urban hydrology, which formed the basis for development of this method. Results also demonstrate that the proposed method is functionally different than existing methods. The distance metric represents an evolutionary step forward, overcomes some difficulties present from other approaches, and would be applicable to a number of cross-disciplinary applications. [ABSTRACT FROM AUTHOR]

This paper deals with the characteristics and effects of digital filter initialization, as implemented in the operational three-dimensional variational data assimilation (3DVAR) system of the Aire Limitéée Adaptation Dynamique Dééveloppement International (ALADIN)-France regional weather forecast model. First, a series of findings on the properties of the initialization of the model are discussed. Examples of initial spinup linked with inertia--gravity wave occurrence are shown, and the major sources for their generation are listed. These experimental results are compared with past and present experiences concerning the use and need for digital filter initialization. Furthermore, the impacts of switching to an incremental formulation of the filter in data assimilation mode are demonstrated. Second, the effects of the filter formulation on the results of an observation impact study are illustrated. The latter consists of implementing screen-level, 10-m horizontal wind information into the ALADIN 3DVAR analysis. There can, indeed, be some delicate interference between observation impact evaluation and the effects of filtering, at least on short-term forecasts. The paper is concluded with some general considerations on the experimental evaluation of spinup and the link between the assimilation system design and model state filtering. [ABSTRACT FROM AUTHOR]

The degrees of freedom for signal (DFS) is used in data assimilation applications to measure the self-sensitivity of analysis to different observation types. This paper describes a practical method to estimate the DFS of observations from a posteriori statistics. The method does not require the consistency of the error statistics in the analysis system and it is shown that the observational impact on analyses can be estimated from observation departures with respect to analysis or the forecast. This method is first introduced to investigate the impact of a complete set, or subsets, of observations on the analysis for idealized one-dimensional variational data assimilation (1D-Var) analysis experiments and then applied in the framework of the three dimensional (3D)- and four-dimensional (4D)-Var schemes developed at Environment Canada. [ABSTRACT FROM AUTHOR]

A large fraction of papers in the climate literature includes erroneous uses of significance tests. A Bayesian analysis is presented to highlight the meaning of significance tests and why typical misuse occurs. The significance statistic is not a quantitative measure of how confident one can be of the ''reality'' of a given result. It is concluded that a significance test very rarely provides useful quantitative information. [ABSTRACT FROM AUTHOR]

Often there is a need to consider spatial weighting in methods for finding spatial patterns in climate data. The focus of this paper is on techniques that maximize variance, such as empirical orthogonal functions (EOFs). A weighting matrix is introduced into a generalized framework for dealing with spatial weighting. One basic principal in the design of the weighting matrix is that the resulting spatial patterns are independent of the grid used to represent the data. A weighting matrix can also be used for other purposes, such as to compensate for the neglect of unrepresented subgrid-scale variance or, in the form of a prewhitening filter, to maximize the signal-to-noise ratio of EOFs. The new methodology is applicable to other types of climate pattern analysis, such as extended EOF analysis and maximum covariance analysis. The increasing availability of large datasets of three-dimensional gridded variables (e.g., reanalysis products and model output) raises special issues for data-reduction methods such as EOFs. Fast, memory-efficient methods are required in order to extract leading EOFs from such large datasets. This study proposes one such approach based on a simple iteration of successive projections of the data onto time series and spatial maps. It is also demonstrated that spatial weighting can be combined with the iterative methods. Throughout the paper, multivariate statistics notation is used, simplifying implementation as matrix commands in high-level computing languages. [ABSTRACT FROM AUTHOR]

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection. [ABSTRACT FROM AUTHOR]

This study explores how the Atlanta, Georgia (United States), urban region influences warm-season (May through September) cloud-to-ground lightning flashes and precipitation. Eight years (1995–2003) of flashes from the National Lightning Detection Network and mean accumulated precipitation from the North American Regional Reanalysis model were mapped under seven different wind speed and direction combinations derived from cluster analysis. Overlays of these data affirmed a consistent coupling of lightning and precipitation enhancement around Atlanta. Maxima in precipitation and lightning shifted in response to changes in wind direction. Differences in the patterns of flash metrics (flash counts versus thunderstorm counts), the absence of any strong urban signal in the flashes of individual thunderstorms, and the scales over which flashes and precipitation enhancement developed are discussed in light of their support for land-cover- and aerosol-based mechanisms of urban weather modification. This study verifies Atlanta’s propensity to conjointly enhance cloud-to-ground lightning and precipitation production in the absence of strong synoptic forcing. However, because of variability in aerosol characteristics and the dynamics of land use change, it may be a simplification to assume that this observed enhancement will be persistent across all scales of analysis. [ABSTRACT FROM AUTHOR]

Water balance calculations are becoming increasingly important for earth-system studies. Precipitation is one of the most critical input variables for such calculations because it is the immediate source of water for the land surface hydrological budget. Numerous precipitation datasets have been developed in the last two decades, but these datasets often show marked differences in their spatial and temporal distribution of this key hydrological variable. This paper compares six monthly precipitation datasets—Climate Research Unit of University of East Anglia (CRU), Willmott–Matsuura (WM), Global Precipitation Climate Center (GPCC), Global Precipitation Climatology Project (GPCP), Tropical Rainfall Measuring Mission (TRMM), and NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2)—to assess the uncertainties in these datasets and their impact on the terrestrial water balance. The six datasets tested in the present paper were climatologically averaged and compared by calculating various statistics of the differences. The climatologically averaged monthly precipitation estimates were applied as inputs to a water balance model to estimate runoff and the uncertainties in runoff arising directly from the precipitation estimates. The results of this study highlight the need for accurate precipitation inputs for water balance calculations. These results also demonstrate the need to improve precipitation estimates in arid and semiarid regions, where slight changes in precipitation can result in dramatic changes in the runoff response due to the nonlinearity of the runoff-generation processes. [ABSTRACT FROM AUTHOR]

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA's latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies. [ABSTRACT FROM AUTHOR]

The Mediterranean is a 'hot spot' for the genesis of different types of severe weather events, including potentially damaging wind phenomena like downbursts, whose occurrence and evolution in this geographical region have not been documented in the literature. This paper is part of an interdisciplinary collaboration between atmospheric scientists and wind engineers with the objective of conducting a comprehensive analysis of the field measurements and weather scenarios related to nonsynoptic wind systems in this area. The downburst that struck the Livorno coast of Italy at about 1310 local time 1 October 2012 is investigated as a relevant test case for such severe wind events. The wind velocity records detected by ultrasonic anemometers, part of a monitoring network created for the European 'Wind and Ports' and 'Wind, Ports and Sea' projects, are analyzed and decomposed in order to inspect the main statistical features of this transient event. The analysis of the meteorological precursors to this event is carried out making use of model analyses, standard in situ measurements, remote sensing techniques, proxy data, and direct observations. The results obtained bring new insights into a downburst's onset and detection in the Mediterranean, its evolution at the local scale, and possible connections to specific synoptic-scale weather conditions like secondary cyclogenesis in the lee of the Alps. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors (AMVs) are derived operationally at EUMETSAT from the AVHRR/3 instrument on the Polar System satellite MetOp-A since 2011. The launch of MetOp-B in 2012 allowed for doubling of the production of AMVs over the polar regions using both MetOp-A and MetOp-B satellite data. In addition to the single AVHRR polar wind product, in 2014 EUMETSAT developed a new global AVHRR wind product extracted from a pair of MetOp-A and MetOp-B images. This new product is extracted using the large overlap in the imagery data obtained from the tandem configuration of the two satellites on the same orbital plane but with a phase difference of about 50 min. The tandem configuration also provides the possibility to derive wind vectors over polar areas using a triplet of AVHRR images, keeping the same time period necessary to derive the single MetOp polar wind product but allowing for a temporal consistency check in the calculation of the AMV quality index. Three different AMV products are currently extracted from AVHRR imagery at EUMETSAT, using two or three images taken by one or two satellites having different coverage and time integration. This paper describes the scientific concept of the AVHRR wind extraction algorithm developed at EUMETSAT and presents the performances of the various AVHRR wind products. Intercomparisons of these different products highlight the role of the temporal gap between the images used to extract the wind and the impact of the consistency check on the calculation of the quality index. [ABSTRACT FROM AUTHOR]

It is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors' experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar data are available. As a result, the 3DVAR analysis with the constraint is more accurate when verified against an independent dataset. The performance of this new constraint on improving precipitation forecasts is tested using six convective cases and verified against radar-derived precipitation by employing four skill indices. All of the skill indices show improved forecasts when using the methodology presented in this paper. [ABSTRACT FROM AUTHOR]

In early November 2006, an unnamed tropical cyclone (TC) formed via the tropical transition (TT) process at 42°N over the eastern North Pacific. An extratropical cyclone (EC), developing downstream of a thinning upper-tropospheric trough over the eastern North Pacific, served as the precursor disturbance that would ultimately undergo TT. The TT of the unnamed TC was extremely unusual -occurring over ~16°C sea surface temperatures in a portion of the eastern North Pacific basin historically devoid of TC activity. This paper 1) identifies the upper- and lower-tropospheric features linked to the formation of the EC that transitions into the unnamed TC, 2) provides a synoptic overview of the features and processes associated with the unnamed TC's TT, and 3) discusses the landfall of the weakening cyclone along the west coast of North America. As observed in previous studies of TT, the precursor EC progresses through the life cycle of a marine extratropical frontal cyclone, developing a bent-back warm front on its northern and western sides and undergoing a warm seclusion process. Backward air parcel trajectories suggest that air parcels isolated in the center of the transitioning cyclone were warmed in the lower troposphere via sensible heating from the underlying sea surface. Vertical cross sections taken through the center of the cyclone during its life cycle reveal its transformation from an asymmetric, cold-core, EC into an axisymmetric, warm-core, TC during TT. Ensemble reforecasts initialized after TT highlight the relatively low forecast skill associated with the landfall of the weakening cyclone. [ABSTRACT FROM AUTHOR]

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation. [ABSTRACT FROM AUTHOR]

Edward Kidson, Director of the Meteorological Service of New Zealand from 1927 until his death in 1939, was an instrumental figure in modernizing Australasian meteorology. Throughout the 1920s, Kidson promoted the methods of synoptic analysis emanating from the Bergen School of Meteorology. However, it was not until the 1930s that he began in earnest to apply these methods to weather charts for the Australasian region. This development was aided by two visits he made to Bergen and by a personal correspondence he maintained with Jacob Bjerknes during the 1930s. In 1932, Kidson presented the first Norwegian-style analysis conducted for a Southern Hemisphere region, promptly following this with a more extensive study. However, these analyses were not of a sufficient standard at that stage to be adopted in forecasting practice. It was the fortuitous visit to New Zealand of Norwegian meteorologist Jørgen Holmboe, in 1934, that finally facilitated the transition. Holmboe was attached to the Lincoln Ellsworth Antarctic Expedition, but damage to their aircraft caused them to spend the winter of 1934 in New Zealand. Holmboe was engaged at the Meteorological Service in Wellington during this period, working with Kidson to apply Norwegian methods to the region. Kidson had hoped to further embed this practice by employing Tor Bergeron in New Zealand during 1938. Bergeron had accepted an offer from Kidson but canceled at the last minute after contracting rheumatic fever. Nevertheless, shortly after Holmboe’s visit, daily analyses were being conducted along Norwegian lines, bringing Australasian meteorology into the twentieth century. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985-2008) and 25-yr (1985-2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April-June. [ABSTRACT FROM AUTHOR]

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%-82% and 60%-68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist. [ABSTRACT FROM AUTHOR]

This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98-2006/07). The evaluated precipitation products are the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals. [ABSTRACT FROM AUTHOR]

Nearest-neighbor gridding, binning, and bin-averaging procedures are performed routinely to map the irregularly sampled data onto a grid for data analysis and assimilation. Because these procedures are actually an interpolation procedure based on a piecewise constant function as the interpolation kernel, they tend to discard the subgrid locations of the data. Use of a locally continuous function for the interpolation kernel can preserve the subgrid location information in the data, at the cost of numerical sensitivity to the spatial variation in data density. This paper suggests a simple numerical procedure, based on a single correlation coefficient parameter, to eliminate such numerical sensitivity. [ABSTRACT FROM AUTHOR]

The flux of water vapor due to advection is measured using high-resolution Raman lidar that was orientated horizontally across a land-lake transition. At the same time, a full surface energy balance is performed to assess the impact of scalar advection on energy budget closure. The flux of water vapor due to advection is then estimated with analytical solutions to the humidity transport equation that show excellent agreement with the field measurements. Although the magnitude of the advection was not sufficient to account for the total energy deficit for this field site, the analytical approach is used to explore situations where advection would be the dominant transport mechanism. The authors find that advection is at maximum when the measurement height is 0.036 times the distance to a land surface transition. The framework proposed in this paper can be used to predict the potential impact of advection on surface flux measurements prior to field deployment and can be used as a data analysis algorithm to calculate the flux of water vapor due to advection from field measurements. [ABSTRACT FROM AUTHOR]

Doppler velocity measurements from airborne meteorological Doppler radars require removal of the aircraft motion contribution in order to provide radial velocity of hydrometeor targets. This is a critical step for hydrometeor motion and wind retrievals. The aircraft motion contribution is defined as the scalar product between the radar antenna beam-pointing vector and the aircraft velocity vector at the antenna phase center. The accuracy in the removal of the aircraft velocity contribution is determined by the accuracy of the beam-pointing vector, the rigidity of the antenna mount, and the accuracy of the aircraft attitude and velocity measurements. In this paper an optimization technique is proposed to determine the antenna beam-pointing vector and to analyze its uncertainties using aircraft attitude and velocity data from a GPS-aided inertial measurement unit and radar observations of the earth surface. The technique is applied to Wyoming Cloud Radar (WCR) on the University of Wyoming King Air (UWKA) aircraft. The beam-pointing vectors of the two fixed downward-pointing WCR antennas are calibrated using data selected from several calibration flights. The maximum root-mean-square error in the calibrated beam-pointing angles is smaller than 0.03°, resulting in less than 0.1 m s−1 aircraft motion residual error in the Doppler velocities after removing the aircraft motion contribution. Some applicability and limitations to other airborne Doppler radars with fixed antennas are discussed. [ABSTRACT FROM AUTHOR]

Full exposition of the performance of a set of forecasts requires examination of the joint frequency distribution of those forecasts and their corresponding observations. In settings involving probability forecasts, this joint distribution has a high dimensionality, and communication of its information content is often best achieved graphically. This paper describes an extension of the well-known reliability diagram, which displays the joint distribution for probability forecasts of dichotomous events, to the case of probability forecasts for three disjoint events, such as 'below,' 'near,' and 'above normal.' The resulting diagram, called the calibration simplex, involves a discretization of the 2-simplex, which is an equilateral triangle. Characteristics and interpretation of the calibration simplex are illustrated using both idealized verification datasets, and the 6-10- and 8-14-day temperature and precipitation forecasts produced by the U.S. Climate Prediction Center. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution and temporal changes of the land surface parameters at the Three Gorges Dam (TGD) region is essential to understanding the changes of hydrological processes and climate systems possibly brought by TGD. Based on accumulated observations for years from a spaceborne passive microwave radiometer, this study presents and analyzes the spatial and temporal distribution of soil moisture in the TGD region. Major drought and flood events are identified from the satellite-derived soil moisture products. Moreover, the areas around the largest freshwater lakes of China, the Dongting and Poyang Lakes, are frequently subjected to drought events, which might be partially related to the impoundment of TGD since the year 2006. Data analysis further reveals a statistically significant drying trend in May in the middle and lower reaches of the Yangtze River over the years 2003-11. These analyses indicate that water shortage becomes a realistic challenge for the once water abundant Yangtze River region, and more considerations on the possible consequences brought by climate changes are needed for the operation of TGD. [ABSTRACT FROM AUTHOR]

In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent. [ABSTRACT FROM AUTHOR]

Quantitative information on the spatial and temporal error structures in large-scale (regional or global) precipitation datasets is essential for hydrologic and climatic studies. A powerful tool to quantify error structures in large-scale datasets is triple collocation. In this paper, triple collocation is used to determine the spatial and temporal error characteristics of three precipitation datasets over Europe-that is, the precipitation-properties visible/near infrared (PP-VNIR) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board Meteosat Second Generation (MSG), weather radar observations from the European integrated weather radar system, and gridded rain gauge observations from the datasets of the Global Precipitation Climatology Centre (GPCC) and the European Climate Assessment and Dataset (ECA&D) project. For these datasets the spatial and temporal error characteristics are evaluated and their performance is discussed. Finally, weather radar and PP-VNIR retrievals are used to evaluate the diurnal cycles of precipitation occurrence and intensity during daylight hours for different European climate regions. The results suggest that the triple collocation method provides realistic error estimates. The spatial and temporal error structures agree with the findings of earlier studies and reveal the strengths and weaknesses of the datasets, such as inhomogeneity of weather radar practices across Europe, the effect of sampling density in the gridded rain gauge dataset, and the sensitivity to retrieval assumptions in the PP-VNIR dataset. This study can help us in developing satisfactory strategies for combining various precipitation datasets-for example, for improved monitoring of diurnal variations or for detecting temporal trends in precipitation. [ABSTRACT FROM AUTHOR]

This paper assesses the performance of the Weather Research and Forecasting Model (WRF) as a tool for multiscale atmospheric simulations. Tests are performed in real and idealized cases with multiple configurations and with resolutions ranging from the mesoscale (gridcell size ~10 km) for the real cases to local scales (gridcell size ~50 m) for both real and idealized cases. All idealized simulations and the finest real-case simulations use the turbulence-resolving large-eddy simulation mode of WRF (WRF-LES). Tests in neutral conditions and with idealized forcing are first performed to assess the model's sensitivity to grid resolutions and subgrid-scale parameterizations and to optimize the setup of the real cases. An increase in horizontal model resolution is found to be more beneficial than an increase in vertical resolution. WRF-LES is then tested, using extensive observational data, in real-world cases over complex terrain through nested simulations in which the mesoscale domains drive the LES domains. Analysis of the mesoscale simulations indicates that the data needed to force the largest simulated domain and to initialize surface parameters have the strongest influence on the results. Similarly, LES model fields are primarily influenced by their mesoscale meteorological forcing. As a result, the nesting of LES models down to a 50-m resolution does not improve all aspects of hydrometeorological predictions. Advantages of using fine-resolution LES are noted at nighttime (under stable conditions) and over heterogeneous surfaces when local properties are required or when resolving small-scale surface features is desirable. [ABSTRACT FROM AUTHOR]

The paper describes the operational analysis of the Imaging Infrared Radiometer (IIR) data, which have been collected in the framework of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation ( CALIPSO) mission for the purpose of retrieving high-altitude (above 7 km) cloud effective emissivity and optical depth that can be used in synergy with the vertically resolved Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) collocated observations. After an IIR scene classification is built under the CALIOP track, the analysis is applied to features detected by CALIOP when found alone in the atmospheric column or when CALIOP identifies an opaque layer underneath. The fast-calculation radiative transfer (FASRAD) model fed by ancillary meteorological and surface data is used to compute the different components involved in the effective emissivity retrievals under the CALIOP track. The track analysis is extended to the IIR swath using homogeneity criteria that are based on radiative equivalence. The effective optical depth at 12.05 μm is shown to be a good proxy for about one-half of the cloud optical depth, allowing direct comparisons with other databases in the visible spectrum. A step-by-step quantitative sensitivity and performance analysis is provided. The method is validated through comparisons of collocated IIR and CALIOP optical depths for elevated single-layered semitransparent cirrus clouds, showing excellent agreement (within 20%) for values ranging from 1 down to 0.05. Uncertainties have been determined from the identified error sources. The optical depth distribution of semitransparent clouds is found to have a nearly exponential shape with a mean value of about 0.5-0.6. [ABSTRACT FROM AUTHOR]

A large aperture scintillometer (LAS) observes the intensity of the atmospheric turbulence across large distances, which is related to the path-averaged sensible heat flux H. In this paper, two problems in the derivation of continuous series of H from LAS data are investigated and the importance of nighttime H fluxes is assessed. First, as an LAS is unable to determine the sign of H, the transition from unstable to stable conditions is evaluated in order to make continuous H series. Therefore, different algorithms to judge the atmospheric stability for an LAS installed over a distance of 9.5 km have been tested. The diurnal cycle of the refractive index structure parameter, , results in the best suitable, operational algorithm. A second issue is the humidity correction for LAS data, which is performed by using the Bowen ratio ( β). As β is taken from ground-based measurements with data gaps, the number of resulting H values is reduced. Not including this humidity correction results in a marginal error in H, but increases the completeness of the H series. Applying these conclusions to the 2-yr time series of the LAS results in an almost-continuous H time series. As the majority of the time steps has been found to be under stable conditions, there is a clear impact of Hstable on H24h-the 24-h average of H. For stable conditions, Hstable values are mostly negative, and hence lower than the H == 0 W m−2 assumption as mostly adopted. For months where stable conditions prevail (winter), H24h is overestimated using this assumption, and calculation of Hstable is recommended. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

The NOAA G-IV aircraft routinely measures vertical aircraft acceleration from the inertial navigation system at 1 Hz. The data provide a measure of turbulence on a 250-m horizontal scale over a layer from 12.8- to 14.8-km elevation. Turbulence in this layer of tropical cyclones was largest by 35%–40% in the inner 200 km of radius and decreased monotonically outward to the 1000-km radius. Turbulence in major hurricanes exceeded that in weaker tropical cyclones. Turbulence data points were divided among three regions of the tropical cyclone: cirrus canopy; outside the cirrus canopy; and a transition zone between them. Without exception, turbulence was greater within the canopy and weaker outside the canopy. Nighttime turbulence exceeded daytime turbulence for all radii, especially within the cirrus canopy, implicating radiative forcing as a factor in turbulence generation. A case study of widespread turbulence in Hurricane Ivan (2004) showed that interactions between the hurricane outflow channel and westerlies to the north created a region of absolute vorticity of −6 × 10−5 s−1 in the upper troposphere. Outflow accelerated from the storm center into this inertially unstable region, and visible evidence for turbulence and transverse bands of cirrus appeared radially inward of the inertially unstable region. It is argued that both cloud-radiative forcing and the development of inertial instability within a narrow outflow layer were responsible for the turbulence. In contrast, a second case study (Isabel 2003) displayed strong near-core turbulence in the presence of large positive absolute vorticity and no local inertial instability. Peak turbulence occurred 100 km downwind of the eyewall convection. [ABSTRACT FROM AUTHOR]

Marginalized particle filtering (MPF), also known as Rao-Blackwellized particle filtering, has been recently developed as a hybrid method combining analytical filters with particle filters. This paper investigates the prospects of this approach in environmental modeling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In the formulation herein, exact marginalization in the MPF is replaced by approximate marginalization, yielding a framework for creation of new hybrid filters. In particular, the authors propose to use the MPF framework for online tuning of nuisance parameters of ensemble filters. Conditional independence-based simplification of the MPF algorithm is proposed for computational reasons and its close relation to previously published methods is discussed. The strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance, and the length scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities, leaving space for further improvements. [ABSTRACT FROM AUTHOR]

The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar-lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50%% in clouds with a reflectivity factor larger than 0 dB Z. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function. [ABSTRACT FROM AUTHOR]

While time and zonal mean budgets of axial angular momentum (AAM) have been presented in pressure coordinates and also in isentropic coordinates, AAM budgets in height coordinates have not been published yet. The results of such an analysis on the basis of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) winter data are presented in this paper, which includes explicitly evaluated vertical eddy fluxes of momentum and mass as new features. As expected, AAM fluxes related to the Hadley cell are dominant. Transient vertical AAM fluxes are directed upward at the midlatitudes. Transient mass transports are not negligible, while triple terms are unimportant. Problems with the global balance of torques acting at the surface are discussed as well as those of mass conservation. [ABSTRACT FROM AUTHOR]