A 27 day persistence model of near-Earth solar wind conditions: a long lead-time forecast and a benchmark for dynamical models

Geomagnetic activity has long been known to exhibit approximately 27 day periodicity, resulting from solar wind structures repeating each solar rotation. Thus a very simple near-Earth solar wind forecast is 27 day persistence, wherein the near-Earth solar wind conditions today are assumed to be identical to those 27 days previously. Effective use of such a persistence model as a forecast tool, however, requires the performance and uncertainty to be fully characterized. The first half of this study determines which solar wind parameters can be reliably forecast by persistence and how the forecast skill varies with the solar cycle. The second half of the study shows how persistence can provide a useful benchmark for more sophisticated forecast schemes, namely physics-based numerical models. Point-by-point assessment methods, such as correlation and mean-square error, find persistence skill comparable to numerical models during solar minimum, despite the 27 day lead time of persistence forecasts, versus 2–5 days for numerical schemes. At solar maximum, however, the dynamic nature of the corona means 27 day persistence is no longer a good approximation and skill scores suggest persistence is out-performed by numerical models for almost all solar wind parameters. But point-by-point assessment techniques are not always a reliable indicator of usefulness as a forecast tool. An event-based assessment method, which focusses key solar wind structures, finds persistence to be the most valuable forecast throughout the solar cycle. This reiterates the fact that the means of assessing the “best” forecast model must be specifically tailored to its intended use.

Research priorities in observing and modeling urban weather and climate

In 2007, the world reached the unprecedented milestone of half of its people living in cities, and that proportion is projected to be 60% in 2030. The combined effect of global climate change and rapid urban growth, accompanied by economic and industrial development, will likely make city residents more vulnerable to a number of urban environmental problems, including extreme weather and climate conditions, sea-level rise, poor public health and air quality, atmospheric transport of accidental or intentional releases of toxic material, and limited water resources. One fundamental aspect of predicting the future risks and defining mitigation strategies is to understand the weather and regional climate affected by cities. For this reason, dozens of researchers from many disciplines and nations attended the Urban Weather and Climate Workshop.1 Twenty-five students from Chinese universities and institutes also took part. The presentations by the workshop's participants span a wide range of topics, from the interaction between the urban climate and energy consumption in climate-change environments to the impact of urban areas on storms and local circulations, and from the impact of urbanization on the hydrological cycle to air quality and weather prediction.

The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.

Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions

There has been a great deal of recent interest in producing weather forecasts on the 2–6 week sub-seasonal timescale, which bridges the gap between medium-range (0–10 day) and seasonal (3–6 month) forecasts. While much of this interest is focused on the potential applications of skilful forecasts on the sub-seasonal range, understanding the potential sources of sub-seasonal forecast skill is a challenging and interesting problem, particularly because of the likely state-dependence of this skill (Hudson et al 2011). One such potential source of state-dependent skill for the Northern Hemisphere in winter is the occurrence of stratospheric sudden warming (SSW) events (Sigmond et al 2013). Here we show, by analysing a set of sub-seasonal hindcasts, that there is enhanced predictability of surface circulation not only when the stratospheric vortex is anomalously weak following SSWs but also when the vortex is extremely strong. Sub-seasonal forecasts initialized during strong vortex events are able to successfully capture the associated surface temperature and circulation anomalies. This results in an enhancement of Northern annular mode forecast skill compared to forecasts initialized during the cases when the stratospheric state is close to climatology. We demonstrate that the enhancement of skill for forecasts initialized during periods of strong vortex conditions is comparable to that achieved for forecasts initialized during weak events. This result indicates that additional confidence can be placed in sub-seasonal forecasts when the stratospheric polar vortex is significantly disturbed from its normal state.

Can site and landscape-scale environmental attributes buffer bird populations against weather events?

Projected impacts of climate change on the populations and distributions of species pose a challenge for conservationists. In response, a number of adaptation strategies to enable species to persist in a changing climate have been proposed. Management to maximise the quality of habitat at existing sites may reduce the magnitude or frequency of climate-driven population declines. In addition large-scale management of landscapes could potentially improve the resilience of populations by facilitating inter-population movements. A reduction in the obstacles to species’ range expansion, may also allow species to track changing conditions better through shifts to new locations, either regionally or locally. However, despite a strong theoretical base, there is limited empirical evidence to support these management interventions. This makes it difficult for conservationists to decide on the most appropriate strategy for different circumstances. Here extensive data from long-term monitoring of woodland birds at individual sites are used to examine the two-way interactions between habitat and both weather and population count in the previous year. This tests the extent to which site-scale and landscape-scale habitat attributes may buffer populations against variation in winter weather (a key driver of woodland bird population size) and facilitate subsequent population growth. Our results provide some support for the prediction that landscape-scale attributes (patch isolation and area of woodland habitat) may influence the ability of some woodland bird species to withstand weather-mediated population declines. These effects were most apparent among generalist woodland species. There was also evidence that several, primarily specialist, woodland species are more likely to increase following population decline where there is more woodland at both site and landscape scales. These results provide empirical support for the concept that landscape-scale conservation efforts may make the populations of some woodland bird species more resilient to climate change. However in isolation, management is unlikely to provide a universal benefit to all species.

Understanding climate change projections for precipitation over western Europe with a weather typing approach

Precipitation over western Europe (WE) is projected to increase (decrease) roughly northward (equatorward) of 50°N during the 21st century. These changes are generally attributed to alterations in the regional large-scale circulation, e.g., jet stream, cyclone activity, and blocking frequencies. A novel weather typing within the sector (30°W–10°E, 25–70°N) is used for a more comprehensive dynamical interpretation of precipitation changes. A k-means clustering on daily mean sea level pressure was undertaken for ERA-Interim reanalysis (1979–2014). Eight weather types are identified: S1, S2, S3 (summertime types), W1, W2, W3 (wintertime types), B1, and B2 (blocking-like types). Their distinctive dynamical characteristics allow identifying the main large-scale precipitation-driving mechanisms. Simulations with 22 Coupled Model Intercomparison Project 5 models for recent climate conditions show biases in reproducing the observed seasonality of weather types. In particular, an overestimation of weather type frequencies associated with zonal airflow is identified. Considering projections following the (Representative Concentration Pathways) RCP8.5 scenario over 2071–2100, the frequencies of the three driest types (S1, B2, and W3) are projected to increase (mainly S1, +4%) in detriment of the rainiest types, particularly W1 (−3%). These changes explain most of the precipitation projections over WE. However, a weather type-independent background signal is identified (increase/decrease in precipitation over northern/southern WE), suggesting modifications in precipitation-generating processes and/or model inability to accurately simulate these processes. Despite these caveats in the precipitation scenarios for WE, which must be duly taken into account, our approach permits a better understanding of the projected trends for precipitation over WE.

The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia

Remotely sensed rainfall is increasingly being used to manage climate-related risk in gauge sparse regions. Applications based on such data must make maximal use of the skill of the methodology in order to avoid doing harm by providing misleading information. This is especially challenging in regions, such as Africa, which lack gauge data for validation. In this study, we show how calibrated ensembles of equally likely rainfall can be used to infer uncertainty in remotely sensed rainfall estimates, and subsequently in assessment of drought. We illustrate the methodology through a case study of weather index insurance (WII) in Zambia. Unlike traditional insurance, which compensates proven agricultural losses, WII pays out in the event that a weather index is breached. As remotely sensed rainfall is used to extend WII schemes to large numbers of farmers, it is crucial to ensure that the indices being insured are skillful representations of local environmental conditions. In our study we drive a land surface model with rainfall ensembles, in order to demonstrate how aggregation of rainfall estimates in space and time results in a clearer link with soil moisture, and hence a truer representation of agricultural drought. Although our study focuses on agricultural insurance, the methodological principles for application design are widely applicable in Africa and elsewhere.

Solar Energy assessment based on weather station data for direct site monitoring in Indonesia

This thesis evaluates different sites for a weather measurement system and a suitable PV- simulation for University of Surabaya (UBAYA) in Indonesia/Java. The weather station is able to monitor all common weather phenomena including solar insolation. It is planned to use the data for scientific and educational purposes in the renewable energy studies. During evaluation and installation it falls into place that official specifications from global meteorological organizations could not be meet for some sensors caused by the conditions of UBAYA campus. After arranging the hardware the weather at the site was monitored for period of time. A comparison with different official sources from ground based and satellite bases measurements showed differences in wind and solar radiation. In some cases the monthly average solar insolation was deviating 42 % for satellite-based measurements. For the ground based it was less than 10 %. The average wind speed has a difference of 33 % compared to a source, which evaluated the wind power in Surabaya. The wind direction shows instabilities towards east compared with data from local weather station at the airport. PSET has the chance to get some investments to investigate photovoltaic on there own roof. With several simulations a suitable roof direction and the yearly and monthly outputs are shown. With a 7.7 kWpeak PV installation with the latest crystalline technology on the market 8.82 MWh/year could be achieved with weather data from 2012. Thin film technology could increase the value up to 9.13 MWh/year. However, the roofs have enough area to install PV. Finally the low price of electricity in Indonesia makes it not worth to feed in the energy into the public grid.

Stability in The Proportion of Harbor Seals Hauled Out Under Locally Ideal Conditions

We monitored the haul-out behavior of 68 radio-tagged harbor seals (Phoca vitulina) during the molt season at two Alaskan haul-out sites (Grand Island, August-September 1994; Nanvak Bay, August-September 2000). For each site, we created a statistical model of the proportion of seals hauled out as a function of date, time of day, tide, and weather covariates. Using these models, we identified the conditions that would result in the greatest proportion of seals hauled out. Although those “ideal conditions” differed between sites, the proportion of seals predicted to be hauled out under those conditions was very similar (81.3% for Grand Island and 85.7% for Nanvak Bay). The similar estimates for both sites suggest that haul-out proportions under locally ideal conditions may be constant between years and geographic regions, at least during the molt season.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown. (C) 2012 Elsevier B.V. All rights reserved.

Atmospheric radiative transfer in multiple scattering conditions. Application to NWP models.

High spectral resolution radiative transfer (RT) codes are essential tools in the study of the radiative energy transfer in the Earth atmosphere and a support for the development of parameterizations for fast RT codes used in climate and weather prediction models. Cirrus clouds cover permanently 30% of the Earth's surface, representing an important contribution to the Earth-atmosphere radiation balance. The work has been focussed on the development of the RT model LBLMS. The model, widely tested in the infra-red spectral range, has been extended to the short wave spectrum and it has been used in comparison with airborne and satellite measurements to study the optical properties of cirrus clouds. A new database of single scattering properties has been developed for mid latitude cirrus clouds. Ice clouds are treated as a mixture of ice crystals with various habits. The optical properties of the mixture are tested in comparison to radiometric measurements in selected case studies. Finally, a parameterization of the mixture for application to weather prediction and global circulation models has been developed. The bulk optical properties of ice crystals are parameterized as functions of the effective dimension of measured particle size distributions that are representative of mid latitude cirrus clouds. Tests with the Limited Area Weather Prediction model COSMO have shown the impact of the new parameterization with respect to cirrus cloud optical properties based on ice spheres.

Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model

This study examines how different microphysical parameterization schemes influence orographically induced precipitation and the distributions of hydrometeors and water vapour for midlatitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF single-moment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49 W m−2 in the downwelling shortwave radiation and up to 33 W m−2 in the downwelling longwave radiation.

A climatological analysis of high-precipitation events in Dronning Maud Land, Antarctica, and associated large-scale atmospheric conditions

The link between high precipitation in Dronning Maud Land (DML), Antarctica, and the large-scale atmospheric circulation is investigated using ERA-Interim data for 1979–2009. High-precipitation events are analyzed at Halvfarryggen situated in the coastal region of DML and at Kohnen Station located in its interior. This study further includes a comprehensive comparison of high precipitation in ERA-Interim with precipitation data from the Antarctic Mesoscale Prediction System (AMPS) and snow accumulation measurements from automatic weather stations (AWSs), with the limitations of such a comparison being discussed. The ERA-Interim and AMPS precipitation data agree very well. However, the correspondence between high precipitation in ERA-Interim and high snow accumulation at the AWSs is relatively weak. High-precipitation events at both Halvfarryggen and Kohnen are typically associated with amplified upper level waves. This large-scale atmospheric flow pattern is preceded by the downstream development of a Rossby wave train from the eastern South Pacific several days before the precipitation event. At the surface, a cyclone located over the Weddell Sea is the main synoptic ingredient for high precipitation both at Halvfarryggen and at Kohnen. A blocking anticyclone downstream is not a requirement for high precipitation per se, but a larger share of blocking occurrences during the highest-precipitation days in DML suggests that these blocks strengthen the vertically integrated water vapor transport (IVT) into DML. A strong link between high precipitation and the IVT perpendicular to the local orography suggests that IVT could be used as a “proxy” for high precipitation, in particular over DML's interior.

Minimum carbon payment along an aridity gradient for dryland afforestation in non annex I countries with stochastic weather and prices

Recent findings demonstrate that trees in deserts are efficient carbon sinks. It remains however unknown whether the Clean Development Mechanism will accelerate the planting of trees in Non Annex I dryland countries. We estimated the price of carbon at which a farmer would be indifferent between his customary activity and the planting of trees to trade carbon credits, along an aridity gradient. Carbon yields were simulated by means of the CO2FIX v3.1 model for Pinus halepensis with its respective yield classes along the gradient (Arid – 100mm to Dry Sub Humid conditions – 900mm). Wheat and pasture yields were predicted on somewhat similar nitrogen-based quadratic models, using 30 years of weather data to simulate moisture stress. Stochastic production, input and output prices were afterwards simulated on a Monte Carlo matrix. Results show that, despite the high levels of carbon uptake, carbon trading by afforesting is unprofitable anywhere along the gradient. Indeed, the price of carbon would have to raise unrealistically high, and the certification costs would have to drop significantly, to make the Clean Development Mechanism worthwhile for non annex I dryland countries farmers. From a government agency's point of view the Clean Development Mechanism is attractive. However, such agencies will find it difficult to demonstrate “additionality”, even if the rule may be somewhat flexible. Based on these findings, we will further discuss why the Clean Development Mechanism, a supposedly pro-poor instrument, fails to assist farmers in Non Annex I dryland countries living at minimum subsistence level.