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Random uncertainties and vertical error correlations are estimated for three independent data sets. The three collocated data sets are (1) refractivity profiles of radio occultation measurements retrieved from the Metop-A and B and COSMIC-1 missions, (2) refractivity derived from GRUAN-processed RS92 sondes, and (3) refractivity profiles derived from ERA5 forecast fields. The analysis is performed using a generalization of the so-called three-cornered hat method to include off-diagonal elements such that full error covariance matrices can be calculated. The impacts from various sources of representativeness error on the uncertainty estimates are analysed. The estimated refractivity uncertainties of radio occultations, radiosondes, and model data are stated with reference to the vertical representation of refractivity in these data sets. The existing theoretical estimates of radio occultation uncertainty are confirmed in the middle and upper troposphere and lower stratosphere, and only little dependence on latitude is found in that region. In the lower troposphere, refractivity uncertainty decreases with latitude. These findings have implications for both retrieval of tropospheric humidity from radio occultations and for assimilation of radio occultation data in numerical weather prediction models and reanalyses.

Historically, retrieving the detailed structure of atmospheric temperature trends from observations has been demanding. For decades, observations of upper-air temperature have either lacked the necessary vertical resolution, or the horizontal coverage. This has resulted in limited knowledge about the important transition zone around the tropopause. Recent advances in satellite measurement techniques provide new insight into the thermal structure of the upper troposphere/lower stratosphere region. This is a prerequisite for understanding the complex processes of this part of the atmosphere. With unprecedented resolution, latest climate observations from GPS Radio Occultation satellites reveal a significant warming of the atmosphere. The tropical upper troposphere has already warmed about 1 K in the 21st century alone, and the stratospheric trend structure indicates a possible change in stratospheric circulation.

Many studies have by now demonstrated the accuracy of GNSS Radio Occultation (RO) data, and their usefulness as a stable climate reference. Homogeneity of the data records are obtained by reprocessing of the data using uniform processing software throughout the length of the climate record. Version 1 of the ROM SAF Climate Data Record (CDR), based on Metop, CHAMP, GRACE, and COSMIC data, covers a continuous 15-year period from 2002 to 2016. The CDR is extended in time by an Interim CDR (ICDR) which is regularly updated nearly up to present time, and the combined time series is now long enough (20 years) to begin detection of climate trends. We here present results from recent climate applications of RO data: bending angle and temperature trends in the upper troposphere and stratosphere, including the first RO contribution to the IPCC 6th Assessment Report. Furthermore, we outline ROM SAF plans for generating RO climate data records. This includes calculating uncertainty estimates and generating homogenized water vapour climate data records based on a one-dimensional variational retrieval using detrended background data.The Radio Occultation Meteorology Satellite Application Facility (ROM SAF) is a decentralized operational processing facility under EUMETSAT. The main objective of the ROM SAF is to generate and deliver operational radio occultation products from GNSS RO instruments onboard Metop, Metop-SG, Sentinel-6 Michael Freilich and other satellites for NWP and climate applications. The ROM SAF CDR and ICDR is publically available from: http://www.romsaf.org. Further information about the ROM SAF products and services are available at this website.

The Radio Occultation (RO) technique is based on measurements of phase shifts of GNSS radio waves by an instrument onboard a low-Earth orbiting satellite. The processing of the measurements yields the refractive index of the Earth’s atmosphere, from which the temperature, pressure, and humidity fields can be retrieved. It is a limb-sounding technique, with a high vertical resolution, and with observational information retrieved from near-surface to the upper stratosphere. Numerous studies have demonstrated the accuracy of GNSS Radio Occultation (RO) data, and their usefulness as a stable climate reference. Homogeneity of the data records are obtained by reprocessing of the data using uniform processing software and a priori data throughout the length of the climate record. We here present results from a validation of the 17-year ROM SAF RO Climate Data Record (CDR), based on a new reprocessing of Metop, CHAMP, GRACE, and COSMIC data using excess-phase and amplitude data from EUMETSAT (the Metop mission) and UCAR/CDAAC (the CHAMP, GRACE, COSMIC, and Metop missions).A central issue for the generation of RO-based CDRs is whether data from different satellite missions can be combined to form long time series of multi-mission data. This presentation explores the consistency of gridded monthly-mean data from different RO missions through comparison with ERA-Interim reanalysis data, and through a study of mission differences during mission overlap periods. It is shown that within a core region from the upper troposphere to the middle stratosphere, roughly 8 to 35-40 kilometers (depending on latitude and geophysical variable), there is a high consistency amongst the RO missions, allowing for the construction of long-term stable data sets for use in climate studies and climate monitoring.

Temperature changes in the lower and middle stratosphere during 2001-2016 are evaluated using measurements from GPS Radio Occultation (RO) and Advanced Microwave Sounding Unit (AMSU) aboard the Aqua satellite. After downsampling of GPS-RO profiles according to the AMSU weighting functions, the spatially and seasonally resolved trends from the two data sets are in excellent agreement. The observations indicate that the middle stratosphere has cooled in the time period 2002-2016 at an average rate of –0.14±0.12 to –0.36±0.14 K/decade, while no significant change was found in the lower stratosphere. The meridionally and vertically resolved trends from high-resolution GPS-RO data exhibit a marked inter-hemispheric asymmetry and highlight a distinct boundary between tropospheric and stratospheric temperature change regimes matching the tropical thermal tropopause. The seasonal pattern of trend reveals significant opposite-sign structures at high and low latitudes, providing indication of seasonally varying change in stratospheric circulation.

We here present results from an evaluation of the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) gridded monthly mean climate data record (CDR v1.0), based on Global Positioning System (GPS) radio occultation (RO) data from the CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate), and Metop satellite missions. Systematic differences between RO missions, as well as differences of RO data relative to ERA-Interim reanalysis data, are quantified. The methods used to generate gridded monthly mean data are described, and the correction of monthly mean RO climatologies for sampling errors, which is essential for combining data from RO missions with different sampling characteristics, is evaluated. We find good overall agreement between the ROM SAF gridded monthly mean CDR and the ERA-Interim reanalysis, particularly in the 8–30 km height interval. Here, the differences largely reflect time-varying biases in ERA-Interim, suggesting that the RO data record has a better long-term stability than ERA-Interim. Above 30–40 km altitude, the differences are larger, particularly for the pre-COSMIC era. In the 8–30 km altitude region, the observational data record exhibits a high degree of internal consistency between the RO satellite missions, allowing us to combine data into multi-mission records. For global mean bending angle, the consistency is better than 0.04 %, for refractivity it is better than 0.05 %, and for global mean dry temperature the consistency is better than 0.15 K in this height interval. At altitudes up to 40 km, these numbers increase to 0.08 %, 0.11 %, and 0.50 K, respectively. The numbers can be up to a factor of 2 larger for certain latitude bands compared to global means. Below about 8 km, the RO mission differences are larger, reducing the possibilities to generate multi-mission data records. We also find that the residual sampling errors are about one-third of the original and that they include a component most likely related to diurnal or semi-diurnal cycles.

Recently a new approach for the production of GNSS radio occultation climatologies has been proposed. The idea is to perform the averaging of individual profiles already in bending angle space and propagating the mean bending angle profiles through the Abel transform. Climatological products of refractivity, density, pressure, and temperature are directly retrieved from the mean bending angles. The averaging of a large number of profiles suppresses noise in the data, enabling observed bending angle data to be used up to 80 km without the need of a priori information. Above that altitude some background information for the Abel integral is still necessary. This work is a follow up study, having the focus on the comparison of the average profile inversion climatologies (API) from the two processing centers WEGC and DMI, studying monthly COSMIC data from January to March 2011. The impact of different backgrounds above 80 km is tested, and different implementations of the Abel integral are investigated. Results are compared for the climatological products against ECMWF analysis, MIPAS, and SABER data. It is shown that different implementations of the Abel integral have only little impact on the average profile inversion climatologies. On the other hand, different expansions of the bending angle profile above 80 km play a key role on the resulting monthly mean refractivities above 35 km altitude. Below that respective altitude the API climatologies show a good agreement between the two processing centers WEGC and DMI. Due to the downward propagation within the retrieval, effects of the upper initialization lead to differences in dry temperature climatologies already at 20 km altitude. Applying at both centers an exponential extrapolation to the bending angles above 80 km, dry temperature climatologies agree between WEGC, DMI, ECMWF analysis, and MIPAS up to 35 km altitude within ± 0.5 K, and up to 40 km altitude within ± 1 K. We conclude that up to the lower stratosphere the average profile inversion is a valid – and in computation time much faster – alternative for the production of dry atmospheric RO climatologies.

Over the last 15 years, global mean surface temperatures exhibit only weak trends. Recent studies have attempted to attribute this so called temperature hiatus to several causes, amongst them incomplete sampling of the rapidly warming Arctic region. We here examine zonal mean temperature trends in satellite-based tropospheric data sets (based on data from (Advanced) Microwave Sounding Unit and Global Navigation Satellite System Radio Occultation instruments) and in global surface temperatures (HadCRUT4). Omission of successively larger polar regions from the global mean temperature calculations, in both tropospheric and surface data sets, shows that data gaps at high latitudes cannot explain the observed differences between the hiatus and the prehiatus period. Instead, the dominating causes of the global temperature hiatus are found at low latitudes. The combined use of several independent data sets, representing completely different measurement techniques and sampling characteristics, strengthens the conclusions.

Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity climatologies for the stratosphere can be obtained from the Abel inversion of monthly average bending-angle profiles. The averaging of large numbers of profiles suppresses random noise and this, in combination with simple exponential extrapolation above an altitude of 80 km, circumvents the need for a "statistical optimization" step in the processing. Using data from the US–Taiwanese COSMIC mission, which provides ~1500–2000 occultations per day, it has been shown that this average-profile inversion (API) technique provides a robust method for generating stratospheric refractivity climatologies. Prior to the launch of COSMIC in mid-2006, the data records rely on data from the CHAMP (CHAllenging Mini-satellite Payload) mission. In order to exploit the full range of available RO data, the usage of CHAMP data is also required. CHAMP only provided ~200 profiles per day, and the measurements were noisier than COSMIC. As a consequence, the main research question in this study was to see if the average bending-angle approach is also applicable to CHAMP data. Different methods for the suppression of random noise – statistical and through data quality prescreening – were tested. The API retrievals were compared with the more conventional approach of averaging individual refractivity profiles, produced with the implementation of statistical optimization used in the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Radio Occultation Meteorology Satellite Application Facility (ROM SAF) operational processing. In this study it is demonstrated that the API retrieval technique works well for CHAMP data, enabling the generation of long-term stratospheric RO climate data records from August 2001 and onward. The resulting CHAMP refractivity climatologies are found to be practically identical to the standard retrieval at the DMI (Danish Meteorological Institute) below altitudes of 35 km. Between 35 and 50 km, the differences between the two retrieval methods started to increase, showing largest differences at high latitudes and high altitudes. Furthermore, in the winter hemisphere high-latitude region, the biases relative to ECMWF (European Centre for Medium-range Weather Forecasts) were generally smaller for the new approach than for the standard retrieval.

In recent years several aeromagnetic surveys were carried out in Greenland and more will be carried out in the future. We describe some of the characteristics pertinent to surveys in Greenland and the problems faced and experiences made by the survey teams working there, with special emphasis on the west coast where most surveys were conducted. Both unfavorable terrestrial weather and space weather appear to complicate survey planning. We discuss possible options available to the survey teams for mitigating the adverse effect of part of the problems, namely survey data contamination by intense geomagnetic activity. The implementation of a prototype geomagnetic activity forecast service as an aid to planning survey flights is discussed in more detail. The forecast service was tested by an independent observer, and the performance of the scheme is evaluated by a subsequent comparison between forecast and actual measurements. The comparison rendered largely acceptable results, but their validity is limited by the fact that the two-month test interval was characterized by a mostly relatively quiet magnetic field.

The latitudinal distributions of horizontal geomagnetic variations, ΔH, and their time derivatives, ∂H/∂t, were analysed statistically over the three-year period 2003–2005. It appears that the amplitude distributions of horizontal geomagnetic variations and their time derivatives differ systematically between different geomagnetic latitudes and storm intensity levels. We show that the magnetic field variations observed at auroral and polar cap latitudes are under all geomagnetic storm levels comparable in amplitude (in a statistical sense) while they are smaller at subauroral latitudes. In contrast, their time derivatives are clearly the largest at auroral latitudes at all storm levels. These distributions determine in a general sense where and with which probability technological systems and operational procedures may be affected by geomagnetic storms. However, one observes in individual cases that the peak ∂H/∂t (the largest in all horizontal directions) is not necessarily the one which triggers a power system blackout.

Information about the intensity and spatio-temporal characteristics of geomagnetic activity is of interest to aeromagnetic surveyors because a successful magnetostatic anomaly survey relies on the ability to distinguish between spatial and temporal magnetic variations. The latter are usually recorded at a fixed reference magnetometer station. We examined about six months of data collected with the Greenland west coast magnetometer chain at 1-s sampling rate and investigate to which extent temporal geomagnetic variations in selected frequency bands (1, 10 and 100 mHz) are correlated between neighboring sites (which are spaced by 190 km on the average). It appears that the differences between geomagnetic total field variations recorded at neighboring stations are significantly smaller than the magnitudes of the variations themselves. We further set a threshold of 20 nT for very quiet conditions and find that in general broadband total field variations exceed this threshold almost twice as often as the differences between geomagnetic variations at neighboring sites.

Many scientific and commercial activities require an undisturbed geomagnetic field. The Danish Meteorological Institute currently offers real-time magnetometer data as a service to such activities in Denmark and the North Sea region. As a part of an ongoing project aiming at the development of geomagnetic forecast services, we have investigated the statistics of geomagnetic disturbances at the Brorfelde Geomagnetic Observatory in Denmark, with a focus on large-magnitude disturbances that might affect directional-drilling operations in the North Sea. We here report on the distribution of such disturbances as a function of season, time-of-day, and phase of the solar cycle. We demonstrate a close association with strong magnetic storms identified from significant depressions of the ring current index Dst, which have been shown to be caused primarily by coronal mass ejections (CMEs). Very few of the weaker storms, even though much more frequently occurring, generate disturbances exceeding the large-magnitude thresholds, here defined from an actual example of a directional-drilling campaign in the North Sea. Limiting the range of acceptable geomagnetic disturbances would, however, make the weaker storms play a more important role. The prospects of forecasting geomagnetic disturbances are briefly discussed. 2005 COSPAR. Published by Elsevier Ltd. All rights reserved.

To make deterministic medium-range (1 to 3 days ahead) forecasts would require prediction of the detailed near-Earth solar-wind conditions – including the out-of-ecliptic interplanetary magnetic field component – days ahead from remote observations of the inner heliosphere. This is, however, not feasible and will not be in the foreseeable future. Nevertheless, important steps can be taken towards a more physics based approach to geomagnetic forecasting. In this report from an ongoing study within the ESA Space Weather Applications Pilot Project, we discuss presently used concepts and new ideas for fully automatic provision of geomagnetic activity forecasts hours to days ahead. Observations of eruptive events on the Sun are nowadays routinely used as predictors of strong ( Dst 10 MeV solar energetic particles as a discriminator of potentially geoeffective halo coronal mass ejections, substantially reducing the false alarm rate. We also describe how low speed/high speed solar-wind stream interfaces can be automatically detected in currently available solar-wind models, and point out that, in the absence of eruptive events on the Sun, these may be used as predictors of moderate ( Dst

Recent analyses of the NCEP/NCAR re-analysis data have demonstrated statistically significant variations in tropospheric temperatures, geopotential heights, water vapour distribution, and global circulation regimes in phase with the solar cycle over the last 44 years. The findings reveal a consistent pattern of atmospheric response to solar variability throughout the low- and mid-latitude troposphere. In order to more firmly establish the reality and nature of the detected Sun–climate relations, and to correctly identify the atmospheric processes involved, independent confirmation from other relevant data sets is warranted. In this paper we present the results of identical analyses of the NCEP/NCAR and ERA40 re-analysis data sets and show that the detected Sun–climate relations are substantially weaker in the ERA40 data than in the NCEP data. We argue that this largely is due to the presence of temporal inhomogeneities in ERA40 having a substantial impact on decadal signals, which strongly affects the observed Sun–climate relations as well as climate trends. The nature of the NCEP-ERA40 differences, their influence on the detection of Sun–climate relations, and their possible causes are discussed.

Neural networks with internal feedback from the hidden nodes to theinput [Elman, 1990 are developed for prediction of the auroralelectrojet index AE from solar wind data. Unlike linear and nonlinearautoregressive moving-average (ARMA) models, such networks are free todevelop their own internal representation of the recurrent statevariables. Further, they do not incorporate an explicit memory for paststates; the memory is implicitly given by the feedback structure of thenetworks. It is shown that an Elman recurrent network can predict around70 of the observed AE variance using a single sample of solar winddensity, velocity, and magnetic field as input. A neural network withidentical solar wind input, but without a feedback mechanism, onlypredicts around 45 of the AE variance. It is also shown that fourrecurrent state variables are optimal: the use of more than four hiddennodes does not improve the predictions, but with less than that theprediction accuracy drops. This provides an indication that theglobal-scale auroral electrojet dynamics can be characterized by a smallnumber of degrees of freedom. (Less)

This study shows how locally observed geomagnetic disturbances can bepredicted from solar wind data with artificial neural network (ANN)techniques. After subtraction of a secularly varying base level, thehorizontal components XSq and YSq of the quiettime daily variations are modeled with radial basis function networkstaking into account seasonal and solar activity modulations. Theremaining horizontal disturbance components DeltaX and DeltaY aremodeled with gated time delay networks taking local time and solar winddata as input. The observed geomagnetic field is not used as input tothe networks, which thus constitute explicit nonlinear mappings from thesolar wind to the locally observed geomagnetic disturbances. The ANNsare applied to data from Sodankyla Geomagnetic Observatory locatednear the peak of the auroral zone. It is shown that 73% of the DeltaXvariance, but only 34% of the DeltaY variance, is predicted from asequence of solar wind data. The corresponding results for prediction ofall transient variations XSq+DeltaX andYSq+DeltaY are 74% and 51%, respectively. The local timemodulations of the prediction accuracies are shown, and the qualitativeagreement between observed and predicted values are discussed. If drivenby real-time data measured upstream in the solar wind, the ANNs heredeveloped can be used for short-term forecasting of the locally observedgeomagnetic activity. (Less)

Geomagnetic storms and substorms develop under strong control of the solar wind. This is demonstrated by the fact that the geomagnetic activity indices Dst and AE can be predicted from the solar wind alone. A consequence of the strong control by a common source is that substorm and storm indices tend to be highly correlated. However, a part of this correlation is likely to be an effect of internal magnetospheric processes, such as a ring-current modulation of the solar wind-AE relation. The present work extends previous studies of nonlinear AE predictions from the solar wind. It is examined whether the AE predictions are modulated by the Dst index.This is accomplished by comparing neural network predictions from Dst and the solar wind, with predictions from the solar wind alone. Two conclusions are reached: (1) with an optimal set of solar-wind data available, the AE predictions are not markedly improved by the Dst input, but (2) the AE predictions are improved by Dst if less than, or other than, the optimum solar-wind data are available to the net. It appears that the solar wind-AE relation described by an optimized neural net is not significantly modified by the magnetosphere's Dst state. When the solar wind alone is used to predict AE, the correlation between predicted and observed AE is 0.86, while the prediction residual is nearly uncorrelated to Dst. Further, the finding that Dst can partly compensate for missing information on the solar wind, is of potential importance in operational forecasting where gaps in the stream of real time solar-wind data are a common occurrence.Key words. Magnetospheric physics (solar wind · magnetosphere interactions; storms and substorms)

Aims. 'Earthshine' is the dim light seen on the otherwise dark side of the Moon, particularly when it is close to new. 'Earthlight', or reflected sunlight from the Earth, is the source of Earthshine. Using B and V band CCD images of both the dark and bright side of the Moon, we aim to estimate the Johnson photometry B-V colour of the Earthshine for the first time since the late 1960s. From these measurements we are also able to quantify the colour of Earthlight. Methods. We present images of the Moon taken with a small refractor in Hawaii, in B and V bands and taken under favourable conditions so that scattered light in both bands almost completely cancels, yielding a map of the surface in B-V colour. Co-addition of 100 such images taken in rapid succession substantially improves the signal to noise ratio, and several sources of photometric bias are eliminated by use of relative methods. Results. The earthlit dark side of the Moon is observed to be 0.150 +/- 0.005 mag bluer in B-V than the sunlit bright side, in good agreement with the only known previous measurement of this quantity from 1967. Arguing on the basis of the change in B - V for sunlight reflected once o? the Moon, we derive a colour for earthlight of B-V = 0.44+/-0.02 mag (without applying a small, uncertain, phase-dependent reddening correction). The absence of a colour-gradient in the B-V image implies that the scattering properties of the atmosphere+optical system are almost exactly matched in the two wavelength bands, the consequences of which are discussed.

The dissipative processes in the Earth's magnetosphere, such as the ring current and the auroral electrojets, depend on both the external solar wind forcing and factors internal to the magnetosphere. Previous studies have shown that artificial neural networks are able to compute the ring current index Dst very accurately from only solar wind data. In this study, we use neural networks to model the response of the auroral electrojets to the solar wind conditions. The solar wind input to the networks consist of 5-min averaged data from the Earth-orbiting spacecraft IMP 8, while the output is the auroral electrojet index AE. The relationships between the solar wind and the AE index, as modeled by the neural networks, are investigated in a parameter study. The relative importance of individual solar wind variables is studied, as well as the abilities of various coupling functions. It is shown that the use of individual solar wind variables as input to a neural network is superior to the use of corresponding coupling functions. The nonlinear neural networks are related to earlier linear techniques, and the abilities of linear networks (linear filters) are compared to those of nonlinear networks. It is found that a nonlinear network with n, V, By, and B z as input during 100 min can account for 76% of the variance (r≃0.87) in the AE index. No influence of B x is found. With the coupling function p 1/2 V 2 B S as input to a nonlinear network, 71% of the AE index variance is predicted. These results are averaged over a large test set (∼ 330 hours) of data not used to train the networks. The test data are from 1973-1974 and include a diverse set of conditions, ranging from almost quiet to exceptionally disturbed.

We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include solar-wind density n, velocity V and the southward component Bz of the interplanetary magnetic field. Dst is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed non-linear relations. In this study we especially consider the influence of varying the temporal size of the input-data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the input-data sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss processes, and is very much dependent on the ring-current history, and thus also the solar-wind history. With due consideration of the time history when optimizing the networks, we can reproduce 84% of the Dst variance.

[1] To examine the claim that Global Positioning System (GPS) radio occultation (RO) data are useful as a benchmark data set for climate monitoring, the structural uncertainties of retrieved profiles that result from different processing methods are quantified. Profile-to-profile comparisons of CHAMP (CHAllenging Minisatellite Payload) data from January 2002 to August 2008 retrieved by six RO processing centers are presented. Differences and standard deviations of the individual centers relative to the inter-center mean are used to quantify the structural uncertainty. Uncertainties accumulate in derived variables due to propagation through the RO retrieval chain. This is reflected in the inter-center differences, which are small for bending angle and refractivity increasing to dry temperature, dry pressure, and dry geopotential height. The mean differences of the time series in the 8 km to 30 km layer range from −0.08% to 0.12% for bending angle, −0.03% to 0.02% for refractivity, −0.27 K to 0.15 K for dry temperature, −0.04% to 0.04% for dry pressure, and −7.6 m to 6.8 m for dry geopotential height. The corresponding standard deviations are within 0.02%, 0.01%, 0.06 K, 0.02%, and 2.0 m, respectively. The mean trend differences from 8 km to 30 km for bending angle, refractivity, dry temperature, dry pressure, and dry geopotential height are within ±0.02%/5 yrs, ±0.02%/5 yrs, ±0.06 K/5 yrs, ±0.02%/5 yrs, and ±2.3 m/5 yrs, respectively. Although the RO-derived variables are not readily traceable to the international system of units, the high precision nature of the raw RO observables is preserved in the inversion chain.

The possibility of simplifying the retrieval scheme required to produce GNSS radio occultation refractivity climatologies is investigated. In a new, simplified retrieval approach, the main statistical analysis is performed in bending angle space and an estimate of the average bending angle profile is then propagated through an Abel transform. The average is composed of means and medians of ionospheric corrected bending angles up to 80 km. Above that, the observed profile is exponentially extrapolated to infinity using a fixed a priori scale height. The new approach circumvents the need to introduce a "statistical optimization" processing step in which individual bending-angle profiles are merged with a priori data, often taken from a climatology. This processing step can be complex, difficult to interpret, and is generally recognized as a potential source of structural uncertainty. The new scheme is compared with the more conventional approach of averaging individual refractivity profiles, produced with the implementation of statistical optimization used in the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) operational processing. It is shown that the two GNSS radio occultation climatologies agree to within 0.1% from 5 km up to 35–40 km, for the three months January, February, and March 2011. During this time period, the new approach also produces slightly better agreement with ECMWF analyses between 40–50 km, which is encouraging. The possible limitations of the new approach caused by mean residual ionospheric errors and low observation numbers are discussed briefly, and areas for future work are suggested.

Equivalent ionospheric currents obtained with the spherical elementary current systems (SECS) method and derived from nearly 100 ground magnetometers spread over North America and Greenland are compared with ionospheric flow vectors measured by the SuperDARN radars during both the summer and winter seasons. This comparison is done over a range of spatial separations, magnetic latitudes, magnetic local times, and auroral electrojet activity to investigate under what conditions the vectors are anti-parallel to one another. Our results show that in general the equivalent ionospheric currents are anti-parallel to the flows and the best results are achieved within the auroral oval during active geomagnetic conditions in the dawn, dusk and noon sectors in the northern hemisphere summer. These results indicate the best anti-parallel alignment occurs when the currents and flows are large and well defined. Factors that may influence the alignment include ionospheric conductivity gradients and quiet time backgrounds. Our results can be used to approximate the macroscopic (∼1000 km) ionospheric convection patterns. The SECS maps represent a value-added product from the raw magnetometer database and can be used for contextual interpretation; they can help with our understanding of magnetosphere-ionosphere coupling mechanisms using ground arrays and the magnetospheric spacecraft data, and they can be used as input for other techniques.

We discuss the processing of GRAS radio occultation (RO) data done at the GRAS Satellite Application Facility. The input data consists of operational near-real time bending angles from December 2010 from the Metop-A satellite operated by EUMETSAT. The data are processed by an Abel inversion algorithm in combination with statistical optimization based on a two-parameter fit to an MSIS climatology. We compare retrieved refractivity to analyses from ECMWF. It is found that for global averages, the mean differences to ECMWF analyses are smaller than 0.2% below 30 km (except near the surface), with standard deviations around 0.5% for altitudes between 8 and 25 km. The current processing is limited by several factors, which are discussed. In particular, the penetration depth for rising occultations is generally poor, which is related to the tracking of the L2 signal. Extrapolation of the difference between the L1 and L2 signals below the altitude where L2 is lost is possible and would generally allow deeper penetration of retrieved refractivity profiles into the lower troposphere.

[1] With data from the Canadian Magnetic Observatory System, Canadian Array for Real time Investigations of Magnetic Activity, Geophysical Institute Magnetometer Array, Greenland, Time History of Events and Macroscale Interactions during Substorms (THEMIS), and Magnetometer Array for Cusp and Cleft Studies ground magnetometer arrays, we applied the state-of-art technique on the basis of spherical elementary currents systems (SECS) developed by Amm and Viljanen (1999) in order to calculate maps of ionospheric equivalent currents over the whole North American auroral region. This study is the first to apply the SECS technique to a large nonrectangular area with widely separated ground magnetometers (∼350 km). For this study we will first demonstrate the validity of the technique using synthetic data and then examine equivalent ionospheric currents associated with a Harang discontinuity for a case study on 10 December 2007. The results show in detail the dynamic evolution of the currents over the entire North American ground magnetometer network. Equivalent ionospheric current (EIC) maps inferred at the minimum resolution of the database, in this case 10 s, can thus be analyzed further in conjunction with near-simultaneous images of the THEMIS all-sky imager mosaics and Super Dual Auroral Radar Network radar data. The EIC maps represent a value-added product from the raw magnetometer database and can be used for contextual interpretation as well as help with our understanding of magnetosphere-ionosphere coupling mechanisms using the ground arrays and the THEMIS spacecraft data.

[1] Mappings of geomagnetic perturbations are shown for different combinations of the solar wind velocity, interplanetary magnetic field (IMF), and dipole tilt angle (season). Average maps were derived separately for the northward, eastward, and vertical (downward) components of the geomagnetic disturbances, using spherical cap harmonics in least error fits of sorted measurements. The source data are obtained from 104 ground-based magnetometer stations in the Northern Hemisphere at geomagnetic latitudes over 40° during the years 1998 through 2001. Contour maps of statistical fits are shown along-side scatter plots of individual measurements in corrected geomagnetic apex coordinates. The patterns are consistent with previous mappings of ionospheric electric potential. Interestingly, the vertical component of the magnetic perturbations closely resembles maps of the overhead, field-aligned currents, including the Northward IMF configuration. The maximum and minimum values from the statistical mappings are graphed to show their changes as a function of southward IMF magnitude, solar wind velocity, and seasons. It is expected that this work will lead to better advance predictions of the geomagnetic perturbations that are based on real-time IMF measurements.

Lund Observatory is presently designing and constructing a robotic telescope dedicated to studies of the Earth's albedo by measuring the ratio between the intensity of the dark and bright sides of the Moon. The telescope will operate both in broadband and narrow-band modes over the entire visible wavelength range and will transmit observational results back to the operation team over the Internet. Design challenges, in particular related to choice of CCD and stray light suppression, are described, together with the design of the optics, control system, and enclosure. Finally we present results from laboratory tests. The telescope will go into operation in the first half of 2011.

The Earthshine telescope project is a collaborative effort between Lund Observatory (LO) in Sweden and The Institute of Meteorology in Demark (DMI) with the purpose of constructing one or more robotic telescopes to record the albedo of the Earth over a long time. The objective is to measure long-term development of the global cloud coverage and reflectivity for climate modeling. A 1% change in the Earth's albedo will result in an average temperature change of 0.5 K of the Earth, calling for high precision of the albedo measurements. This poses strict demands on the telescope design, in particular with respect to suppression of straylight. The paper describes our proposed optical and mechanical design of the Earthshine telescope, and presents a preliminary straylight analysis of the design. (Less)

[1] This work presents a detailed multi-instrument and modeling case study of an ion upflow event occurring over the Sondrestrom incoherent scatter radar on 17 February 2001. This case study is used to demonstrate the effectiveness of a previously developed optical estimator of ion upflow. The optical estimator of ion upflow is applied in a two step process: (1) near-infrared (NIR) optical observations in the 700–850 nm wavelength range are inverted to reconstruct the energy distribution of precipitating electrons then (2) the reconstructed precipitation is applied to a combined fluid-kinetic model of the ionosphere (TRANSCAR) to estimate the plasma response to the auroral particles. Plasma parameters estimated by this procedure are compared with plasma parameters derived from incoherent scatter radar data recorded during the event and are shown to be in reasonably good agreement. However, this study highlights an important limitation of the estimation technique, the lack of a direct measure of the ambient ionospheric state (nighttime plasma density). Means for addressing this limitation are discussed and may eventually allow for estimation of ionospheric state parameters under certain conditions by using optical sensors.

This report results from a contract tasking Danish Meteorological Institute as follows: The Grantee will study the polar ionosphere and develop data to correlate variances of space and atmospheric electromagnetic transmission as well as operation of space and airborne systems. The contractor will use the existing US Government Furnished Digital Ionospheric Sounding System (DISS or three AN/FMQ-12 ionospheric sounders) in Qaanaaq 77 deg 28 min N 69 deg 13 min W, Sondre Stromfjord/Kangerlussuaq 66 deg 55 min N 51 deg 07 min W, and Narsarsuaq 61 deg 20 min N 45 deg 21 min W Greenland. These sounders have been delivered, installed, and operated under State Department Permit to Operate, Permit Number 512-202, valid from 01 Jan 2004 through 31 Dec 2009 titled "Polar Cap and Auroral Ionospheric Dynamics - Satellite and Ground-Based Studies." At the conclusion of this contract, all US Government Furnished Equipment will remain the property of the United States. DMI will provide the following resources and services: 1) Operate the DISS in a combination of routine and special purpose modes: 2) Support the maintenance of DISS in cooperation with AFRL/VSBXI and the primary maintenance contractor. Specifically, DMI will 3) Analysis and quality control of the data obtained from the DISS and other sensors. 4) Provide copies upon request of any or all, raw or processed data gathered by the DISS in a form electronically compatible with AFRL/VS.

[1] Nearly all strong magnetic storms are generated by halo coronal mass ejections (CMEs), but most frontside halo CMEs are not followed by strong storms. Hence additional information is required to discriminate highly geoeffective CMEs from those less geoeffective. There is a tendency for the strongest magnetic storms to be generated by the fastest CMEs, and in the absence of detailed information on the internal structure of an observed CME, the speed is often used to evaluate the risk that a strong storm will follow. We here report on the alternative use of solar energetic particle (SEP) flux as an indicator of the geoeffectiveness of halo CMEs, addressing the question of whether SEP flux is better in this respect than CME speed. On the basis of a list of 137 frontside halo CMEs, we have investigated the relations between CME speed, SEP flux, and strong magnetic storms. We find that enhancements of the ≥10 MeV SEP flux close to CME onset can be used to indicate whether a full halo CME will be followed by Dst below −100 nT within 18 to 72 hours (our definition of a CME–strong magnetic storm association). Ranking the CMEs by speed and by SEP flux enhancement shows that the latter indicator results in a better discrimination between highly geoeffective CMEs and those less geoeffective. The results suggest that SEP flux enhancements may provide a more efficient discrimination than CME speed for any choice of discriminating thresholds, resulting in lower rates of both false alarms and missed predictions.

meteorological data set from which El Nino and volcanic effects are removed together with the effects of any forcing factors that primarily are expressed as trends in the climate data, e.g., tropospheric aerosols or greenhouse gases. This correction of the data results in an increased clarity of the solar signals, and allows us to rule out the two most important competing forcing factors as a cause of the apparent solar effects. We show that significant solar signals are found in all major observables throughout the low- and mid-latitude troposphere, and that a spatially heterogenous response pattern emerges in which water vapor and the large-scale tropospheric circulation systems play important roles. These findings have implications for the question of where and how solar variability affects the climate system.

We investigate correlations between geomagnetic activity indices, theNorth Atlantic Oscillation (NAO), and stratospheric geopotentialheights. It is shown that the correlation between the geomagnetic indexAp and the NAO index is high and significant since about 1970, that itis significant during winter only, that it was not significant beforeabout 1970, and that the correlations are dominated by quasi-decadalscales of variability. Analysis of the spatial pattern of correlations,restricted to the Northern Hemisphere and wintertime, shows thatsignificant correlations between Ap and sea-level pressures and betweenAp and stratospheric geopotential heights are found for the period1973-2000. However, for the period 1949-1972 no significant correlationsare found at the surface while significant correlations still are foundin the stratosphere. This might indicate that a solar forcing, primarilyacting in the stratosphere, is propagating its influence downward in thelater period but not in the earlier. (Less)

[1] We here present a model for real time forecasting of the geomagnetic index Dst. The model consists of a recurrent neural network that has been optimized to be as small as possible without degrading the accuracy. It is driven solely by hourly averages of the solar wind magnetic field component Bz, particle density n, and velocity V, which means that the model does not rely on observed Dst. In an evaluation based on more than 40,000 hours of solar wind and Dst data, it is shown that this model has smaller errors than other models currently in operational use. A complete description of the model is given in an appendix.