Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models

High-resolution, ground-based and independent observations including co-located wind radiometer, lidar stations, and infrasound instruments are used to evaluate the accuracy of general circulation models and data-constrained assimilation systems in the middle atmosphere at northern hemisphere midlatitudes. Systematic comparisons between observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses including the recent Integrated Forecast System cycles 38r1 and 38r2, the NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses, and the free-running climate Max Planck Institute–Earth System Model–Low Resolution (MPI-ESM-LR) are carried out in both temporal and spectral dom ains. We find that ECMWF and MERRA are broadly consistent with lidar and wind radiometer measurements up to ~40 km. For both temperature and horizontal wind components, deviations increase with altitude as the assimilated observations become sparser. Between 40 and 60 km altitude, the standard deviation of the mean difference exceeds 5 K for the temperature and 20 m/s for the zonal wind. The largest deviations are observed in winter when the variability from large-scale planetary waves dominates. Between lidar data and MPI-ESM-LR, there is an overall agreement in spectral amplitude down to 15–20 days. At shorter time scales, the variability is lacking in the model by ~10 dB. Infrasound observations indicate a general good agreement with ECWMF wind and temperature products. As such, this study demonstrates the potential of the infrastructure of the Atmospheric Dynamics Research Infrastructure in Europe project that integrates various measurements and provides a quantitative understanding of stratosphere-troposphere dynamical coupling for numerical weather prediction applications.

Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.

The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market

We examine the time-series relationship between housing prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the housing prices in these eight MSAs, a purchasing power parity finding for the housing prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

"Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

A comparison of techniques for forecasting utilization of Veterans Affairs hospitals

This study demonstrated that accurate, short-term forecasts of Veterans Affairs (VA) hospital utilization can be made using the Patient Treatment File (PTF), the inpatient discharge database of the VA. Accurate, short-term forecasts of two years or less can reduce required inventory levels, improve allocation of resources, and are essential for better financial management. These are all necessary achievements in an era of cost-containment.^ Six years of non-psychiatric discharge records were extracted from the PTF and used to calculate four indicators of VA hospital utilization: average length of stay, discharge rate, multi-stay rate (a measure of readmissions) and days of care provided. National and regional levels of these indicators were described and compared for fiscal year 1984 (FY84) to FY89 inclusive.^ Using the observed levels of utilization for the 48 months between FY84 and FY87, five techniques were used to forecast monthly levels of utilization for FY88 and FY89. Forecasts were compared to the observed levels of utilization for these years. Monthly forecasts were also produced for FY90 and FY91.^ Forecasts for days of care provided were not produced. Current inpatients with very long lengths of stay contribute a substantial amount of this indicator and it cannot be accurately calculated.^ During the six year period between FY84 and FY89, average length of stay declined substantially, nationally and regionally. The discharge rate was relatively stable, while the multi-stay rate increased slightly during this period. FY90 and FY91 forecasts show a continued decline in the average length of stay, while the discharge rate is forecast to decline slightly and the multi-stay rate is forecast to increase very slightly.^ Over a 24 month ahead period, all three indicators were forecast within a 10 percent average monthly error. The 12-month ahead forecast errors were slightly lower. Average length of stay was less easily forecast, while the multi-stay rate was the easiest indicator to forecast.^ No single technique performed significantly better as determined by the Mean Absolute Percent Error, a standard measure of error. However, Autoregressive Integrated Moving Average (ARIMA) models performed well overall and are recommended for short-term forecasting of VA hospital utilization. ^

XCO2 and XCH4 total column measurement during POLARSTERN cruise PS83 (ANT-XXIX/10), north-south gradient

A portable Fourier transform spectrometer (FTS), model EM27/SUN, was deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO2) and methane (XCH4) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO2 and XCH4 while operating the instrument under field conditions onboard the moving platform during a 6-week cruise on the Atlantic from Cape Town (South Africa, 34° S, 18° E; 5 March 2014) to Bremerhaven (Germany, 54° N, 19° E; 14 April 2014). We demonstrate that our solar tracker typically achieved a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO2 and XCH4 retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra, e.g., when the field of view was partially obstructed by ship structures or when the lines-of-sight crossed the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious air-mass dependency. After the campaign, deployment of the spectrometer alongside the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allowed for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects.

Daily polynya area and ice production in the North Water polynya region

The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness < 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km**3 to 275.7 ± 67.4 km**3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes.

Percepción de variabilidad climática, uso de información y estrategias de los agentes frente al riesgo. Análisis de esquemas decisionales en agricultores de la región pampeana argentina

Este artículo indaga acerca de qué elementos de análisis están presentes en las estrategias de los agricultores de las pampas argentinas a la hora de tomar decisiones de producción considerando el factor climático. El énfasis está puesto en cómo perciben la variabilidad climática y qué información manejan acerca de sus perspectivas a mediano plazo. Durante 2005 se entrevistaron a 60 productores, seleccionados de dos zonas pampeanas de diferentes características físicas. 30 personas correspondieron al área central húmeda y 30 personas a un área marginal semiárida. Los resultados del estudio apuntan a caracterizar los esquemas decisionales presentes en las percepciones de los individuos, teniendo en cuenta que su actividad supone una exposición al riesgo. El objetivo de fondo del trabajo de investigación es proponer acciones de comunicación que ayuden a un mejor uso de la información climática, considerando que se trata de una herramienta disponible con gran potencial para dar un soporte más científico a los procedimientos de los agentes productivos y mejorar su rentabilidad económica.

Percepción de variabilidad climática, uso de información y estrategias de los agentes frente al riesgo. Análisis de esquemas decisionales en agricultores de la región pampeana argentina

Este artículo indaga acerca de qué elementos de análisis están presentes en las estrategias de los agricultores de las pampas argentinas a la hora de tomar decisiones de producción considerando el factor climático. El énfasis está puesto en cómo perciben la variabilidad climática y qué información manejan acerca de sus perspectivas a mediano plazo. Durante 2005 se entrevistaron a 60 productores, seleccionados de dos zonas pampeanas de diferentes características físicas. 30 personas correspondieron al área central húmeda y 30 personas a un área marginal semiárida. Los resultados del estudio apuntan a caracterizar los esquemas decisionales presentes en las percepciones de los individuos, teniendo en cuenta que su actividad supone una exposición al riesgo. El objetivo de fondo del trabajo de investigación es proponer acciones de comunicación que ayuden a un mejor uso de la información climática, considerando que se trata de una herramienta disponible con gran potencial para dar un soporte más científico a los procedimientos de los agentes productivos y mejorar su rentabilidad económica.

Percepción de variabilidad climática, uso de información y estrategias de los agentes frente al riesgo. Análisis de esquemas decisionales en agricultores de la región pampeana argentina

Este artículo indaga acerca de qué elementos de análisis están presentes en las estrategias de los agricultores de las pampas argentinas a la hora de tomar decisiones de producción considerando el factor climático. El énfasis está puesto en cómo perciben la variabilidad climática y qué información manejan acerca de sus perspectivas a mediano plazo. Durante 2005 se entrevistaron a 60 productores, seleccionados de dos zonas pampeanas de diferentes características físicas. 30 personas correspondieron al área central húmeda y 30 personas a un área marginal semiárida. Los resultados del estudio apuntan a caracterizar los esquemas decisionales presentes en las percepciones de los individuos, teniendo en cuenta que su actividad supone una exposición al riesgo. El objetivo de fondo del trabajo de investigación es proponer acciones de comunicación que ayuden a un mejor uso de la información climática, considerando que se trata de una herramienta disponible con gran potencial para dar un soporte más científico a los procedimientos de los agentes productivos y mejorar su rentabilidad económica.

Precipitation over sea: Validation against shipboard in-situ measurements

The satellite derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data) and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis data sets have been validated against in-situ precipitation measurements from ship rain gauges and optical disdrometers over the open-ocean by applying a statistical analysis for binary forecasts. For this purpose collocated pairs of data were merged within a certain temporal and spatial threshold into single events, according to the satellites' overpass, the observation and the forecast times. HOAPS detects the frequency of precipitation well, while ERA-Interim strongly overestimates it, especially in the tropics and subtropics. Although precipitation rates are difficult to compare because along-track point measurements are collocated with areal estimates and the numbers of available data are limited, we find that HOAPS underestimates precipitation rates, while ERA-Interim's Atlantic-wide average precipitation rate is close to measurements. However, regionally averaged over latitudinal belts, there are deviations between the observed mean precipitation rates and ERA-Interim. The most obvious ERA-Interim feature is an overestimation of precipitation in the area of the intertropical convergence zone and the southern sub-tropics over the Atlantic Ocean. For a limited number of snow measurements by optical disdrometers it can be concluded that both HOAPS and ERA-Interim are suitable to detect the occurrence of solid precipitation.

Ocean acidification outweighs nutrient effects in structuring seagrass epiphyte communities

1. Developing a framework for assessing interactions between multiple anthropogenic stressors remains an important goal in environmental research. In coastal ecosystems, the relative effects of aspects of global climate change (e.g. CO2 concentrations) and localized stressors (e.g. eutrophication), in combination, have received limited attention. 2. Using a long-term (11 month) field experiment, we examine how epiphyte assemblages in a tropical seagrass meadow respond to factorial manipulations of dissolved carbon dioxide (CO2(aq)) and nutrient enrichment. In situ CO2(aq) manipulations were conducted using clear, open-top chambers, which replicated carbonate parameter forecasts for the year 2100. Nutrient enrichment consisted of monthly additions of slow-release fertilizer, nitrogen (N) and phosphorus (P), to the sediments at rates equivalent to theoretical maximum rates of anthropogenic loading within the region (1.54 g N/m**2/d and 0.24 g P m**2/d). 3. Epiphyte community structure was assessed on a seasonal basis and revealed declines in the abundance of coralline algae, along with increases in filamentous algae under elevated CO2(aq). Surprisingly, nutrient enrichment had no effect on epiphyte community structure or overall epiphyte loading. Interactions between CO2(aq) and nutrient enrichment were not detected. Furthermore, CO2(aq)-mediated responses in the epiphyte community displayed strong seasonality, suggesting that climate change studies in variable environments should be conducted over extended time-scales. 4. Synthesis. The observed responses indicate that for certain locations, global stressors such as ocean acidification may take precedence over local eutrophication in altering the community structure of seagrass epiphyte assemblages. Given that nutrient-driven algal overgrowth is commonly cited as a widespread cause of seagrass decline, our findings highlight that alternate climate change forces may exert proximate control over epiphyte community structure.

Land Surface Temperature continuous time series from SSM/I passive microwave radiometers, over northern latitudes (Latitudes > 45°N), during summer snow free period (2000-2011), with links to NetCDF files

Remote sensing instruments are key players to map land surface temperature (LST) at large temporal and spatial scales. In this paper, we present how we combine passive microwave and thermal infrared data to estimate LST during summer snow-free periods over northern high latitudes. The methodology is based on the SSM/I-SSMIS 37 GHz measurements at both vertical and horizontal polarizations on a 25 km × 25 km grid size. LST is retrieved from brightness temperatures introducing an empirical linear relationship between emissivities at both polarizations as described in Royer and Poirier (2010). This relationship is calibrated at pixel scale, using cloud-free independent LST data from MODIS instruments. The SSM/I-SSMIS and MODIS data are synchronized by fitting a diurnal cycle model built on skin temperature reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The resulting temperature dataset is provided at 25 km scale and at an hourly time step during the ten-year analysis period (2000-2011). This new product was locally evaluated at five experimental sites of the EU-PAGE21 project against air temperature measurements and meteorological model reanalysis, and compared to the MODIS LST product at both local and circumpolar scale. The results giving a mean RMSE of the order of 2.2 K demonstrate the usefulness of the microwave product, which is unaffected by clouds as opposed to thermal infrared products and offers a better resolution compared to model reanalysis.