Satellite Passive Microwave Observations Research Articles

Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall From Satellite Passive Microwave Measurements: Part 2. Overcoming Database Profile Selection Ambiguity by Consideration of Meteorological Control on Microphysics

A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm-mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a proof that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.

Implications of Representing Snowpack Stratigraphy for the Assimilation of Passive Microwave Satellite Observations

Abstract Under certain conditions, passive microwave satellite observations can be used to estimate snow water equivalent (SWE) across large areas, either through direct retrieval or data assimilation. However, the layered character of snowpacks increases the complexities of estimation algorithms. A multilayer model of snowpack stratigraphy that can serve as the forward model of a snow data assimilation system is described and evaluated. The model’s ability to replicate large-scale snowpack layer features is evaluated using observations from the Cold Land Processes Experiment (Colorado, 2002 and 2003) and a 2002 Nome–Barrow snowpit transect [Snow Science Traverse—Alaska Region (SnowSTAR2002)]. The multilayer model linked with a radiative transfer scheme improved the estimation of brightness temperatures both in terms of absolute values and frequency/polarization differences (error reductions ranging from 47% to 72%) relative to a one-layer model with similar, but depth-averaged, physics at the Colorado sites. The two models were also employed along the SnowSTAR2002 transect of snowpit measurements. The general unavailability of meteorological forcings along the transect made the use of coarse-scale reanalysis data necessary to simulate snow properties and microwave radiances. Errors in the precipitation forcings led to overestimation of SWE, and the simulated brightness temperatures from the two models showed large differences, due mostly to the inability of the single-layer model to simulate the observed larger grain sizes. These differences had implications for the estimation of snow depth; assimilation of Special Sensor Microwave Imager (SSM/I) observations into the multilayer model resulted in improved snow depth estimates (RMSEs of 18.1 cm versus 34.1 cm without assimilation), while the single-layer assimilation slightly decreased the estimation skill (RMSEs of 34.1 versus 33.6 cm).

Soil Moisture Retrievals From the WindSat Spaceborne Polarimetric Microwave Radiometer

An existing methodology to derive surface soil moisture from passive microwave satellite observations is applied to the WindSat multifrequency polarimetric microwave radiometer. The methodology is a radiative-transfer-based model that has successfully been applied to a series of (historical) satellite sensors, including the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Brightness temperature observations from the WindSat and AMSR-E radiometers were compared, and the WindSat observations were adjusted to overcome small sensor differences (e.g., frequency, bandwidth, incidence angle, and original sensor calibration procedure). The method to relate Ka-band brightness temperature observations to land surface temperature was adapted to the overpass times of WindSat. Statistical analysis with both satellite-observed and in situ soil moistures indicates that the quality of the newly derived WindSat soil moisture product is similar to that obtained with AMSR-E after the adjustment of the WindSat brightness temperature observations. The average correlation coefficients ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ) between satellite soil moisture and in situ observations are similar for the two satellites with average values of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.60 for WindSat and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.62 for AMSR-E as calculated from 33 sites. On a global scale, the average correlation coefficient between the two satellite soil moisture products is high with a value of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.83. The results of this study demonstrate that soil moisture from WindSat is consistent with existing soil moisture products derived from AMSR-E using the land parameter retrieval model. Therefore, the soil moisture retrievals from these two satellites could easily be combined to increase the temporal resolution of satellite-derived soil moisture observations.

The effect of surface heat fluxes on interannual variability in the spring onset of snow melt in the central Arctic Ocean

The timing of spring snow melt onset (SMO) on Arctic sea ice strongly affects the heat accumulation in snow and ice during the melt season. SMO itself is controlled by surface heat fluxes. Satellite passive microwave (SSM/I) observations show that the apparent melt onset (MO) varies a lot interannually and even over 50–100 km distances. The MO record appeared to be a complex blend of SMO on top of sea ice and opening of leads and polynyas due to divergent sea ice drift. We extracted SMO out of the original MO record using sea ice concentration data. Applying ERA Interim reanalysis, we evaluated the portion of SMO variance explained by radiative and turbulent surface heat fluxes in the period of 1989–2008. The anomaly of the surface net heat flux 1–7 days prior to SMO explained up to 65% of the interannual variance in SMO in the central Arctic. The main term of the net flux was the downward longwave radiation, which explained up to 90% of SMO variance within the western central Arctic. The role of the latent and sensible heat fluxes in earlier/later SMO was not to bring more/less heat to the surface but to reduce/enhance the surface heat loss. Solar radiation was not an important factor alone, but together with other fluxes improved the explained variance of SMO. Local 20‐year SMO trends averaged over the central Arctic Ocean are toward earlier melt by 9 days per decade.

Greenland ice sheet surface melt extent and trends: 1960–2010

AbstractObserved meteorological data and a high-resolution (5 km) model were used to simulate Greenland ice sheet surface melt extent and trends before the satellite era (1960–79) and during the satellite era through 2010°. The model output was compared with passive microwave satellite observations of melt extent. For 1960–2010 the average simulated melt extent was 15 ± 5%. For the period 1960–72, simulated melt extent decreased by an average of 6%, whereas 1973–2010 had an average increase of 13%, with record melt extent in 2010. The trend in simulated melt extent since 1972 indicated that the melt extent in 2010 averaged twice that in the early 1970s. The maximum and mean melt extents for 2010 were 52% (∼9.5 × 105 km2) and 28% (∼5.2 × 105 km2), respectively, due to higher-than-average winter and summer temperatures and lower-than-average winter precipitation. For 2010, the southwest Greenland melt duration was 41–60 days longer than the 1960–2010 average, while the northeast Greenland melt duration was up to 20 days shorter. From 1960 to 1972 the melting period (with a &gt;10% melt extent) decreased by an average of 3 days a−1. After 1972, the period increased by an average of 2 days a−1, indicating an extended melting period for the ice sheet of about 70 days: 40 and 30 days in spring and autumn, respectively.

Thickness sensitivities in the CICE sea ice model

Passive microwave satellite observations of ice extent and concentration form the foundation of sea ice model evaluations, due to their wide spatial coverage and decades-long availability. Observations related to other model quantities are somewhat more limited but increasing as interest in high-latitude processes intensifies. Sea ice thickness, long judged a critical quantity in the physical system, is now being scrutinized more closely in sea ice model simulations as more expansive measurements become available. While albedo is often the first parameter chosen by modelers to adjust simulated ice thickness, this paper explores a set of less prominent parameters to which thickness is also quite sensitive. These include parameters associated with sea ice conductivity, mechanical redistribution, oceanic heat flux, and ice–ocean dynamic stress, in addition to shortwave radiation. Multiple combinations of parameter values can produce the same mean ice thickness using the Los Alamos Sea Ice Model, CICE. One of these “tuned” simulations is compared with a variety of observational data sets in both hemispheres. While deformed ice area compares well with the limited observations available for ridged ice, thickness measurements differ such that the model cannot agree with all of them simultaneously. Albedo and ice–ocean dynamic parameters that affect the turning of the ice relative to the ocean currents have the largest effect on ice thickness, of the parameters tested here. That is, sea ice thickness is highly sensitive to changes in external forcing by the atmosphere or ocean, and therefore serves as a sensitive diagnostic for high-latitude change.

Rainfall estimation from satellite passive microwave observations in the range 89 GHz to 190 GHz

The aim of the present work is to improve the current state of rainfall estimation by proposing a procedure based entirely on the observations made by the US Advanced Microwave Sounding Unit/B (AMSU/B) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. The procedure does not require the integration of additional sensors which could deteriorate the overall spatial and temporal resolution. It exploits both the radiometric observations made at 89 and 150 GHz (window channels) and the ones made in the 183 GHz water vapor band (opaque channels). In particular, the differences between the measurements of some of the AMSU/B channels are analyzed through radiative transfer simulations for the estimation of precipitation over both land and water surfaces and are related to rain rate values in different atmospheric scenarios. The algorithm estimates exhibit a very good agreement with ground‐based (both radar and rain gauge) observations in the detection of rainfall and a reasonably good estimation of rain rate values. The probability of detection of precipitation is 75% and 90% for rain rates greater than 1 mm/h and 5 mm/h, respectively. Furthermore, due to the use of both window and opaque channels, the proposed procedure is able to retrieve light rain and not to misidentify snow‐covered surfaces as rain.

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations During SMEX02

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.

Verification of Hydrometeor Properties Simulated by a Cloud-Resolving Model Using a Passive Microwave Satellite and Ground-Based Radar Observations for a Rainfall System Associated with the Baiu Front

This paper compares satellite microwave radiometer and ground-based radar observations with the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) simulations, using a bulk microphysical parameterization, for a typical rainfall system associated with the Baiu front around the Okinawa Islands, Japan, on 8 June 2004. The JMA-NHM correctly replicated the shape, location and intensity of the precipitation associated with the observed rainfall system. Radar reflectivities and microwave brightness temperatures (TBs) were simulated using output from the JMA-NHM simulations. They were then compared with concurrent corresponding observations by the National Institute of Information and Communications Technology (NICT), CRL Okinawa Bistatic Polarimetric Radar (COBRA), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E). Fairly good agreement was obtained between the simulated and observed reflectivities under the melting layer and TBs at low frequency (18.7 GHz), indicating that the JMA-NHM adequately simulated the amount of liquid hydrometeors. However, the intensity of scattering in the simulations was stronger than that in the COBRA observations above the melting layer and the AMSR-E observations at high frequencies (36.5 and 89.0 GHz). This was due to the fact that the JMA-NHM overestimated the amount and size of snow particles as a result of large depositional growth. The excessive snow contents were reduced by adjusting some of the microphysical processes in the JMANHM: the snowfall speeds were increased and a riming threshold for snow to graupel conversion was changed. These adjustments helped to reduce the amount and size of snow, resulting in further agreement with the COBRA observations. These adjustments also further improved the simulated TBs at high frequencies, especially at 36.5 GHz. However, differences still exist between the simulated and the observed TBs at high frequencies, suggesting that additional adjustment to and improvement of the snow microphysical processes are needed for the application of the model to microwave remote sensing of precipitation.

Passive microwave (SSM/I) satellite predictions of valley glacier hydrology, Matanuska Glacier, Alaska

We advance an approach to use satellite passive microwave observations to track valley glacier snowmelt and predict timing of spring snowmelt‐induced floods at the terminus. Using 37 V GHz brightness temperatures (Tb) from the Special Sensor Microwave Imager (SSM/I), we monitor snowmelt onset when both Tb and the difference between the ascending and descending overpasses exceed fixed thresholds established for Matanuska Glacier. Melt is confirmed by ground‐measured air temperature and snow‐wetness, while glacier hydrologic responses are monitored by a stream gauge, suspended‐sediment sensors and terminus ice velocity measurements. Accumulation area snowmelt timing is correlated (R2 = 0.61) to timing of the annual snowmelt flood peak and can be predicted within ±5 days.

Simulation of Satellite Passive-Microwave Observations in Rainy Atmospheres at the Meteorological Service of Canada

In this paper, a research version of the Meteorological Service of Canada (MSC) global environmental multiscale (GEM) mesoglobal weather forecasts model is evaluated by comparing the simulated special sensor microwave/imager brightness temperatures (Tbs) with the observed ones. Several comparisons based on two 15-day periods, one in winter and one in summer, have been done. Results are compared to those obtained for a study conducted on the European Centre for Medium-Range Weather Forecast (ECMWF) model. The overall performance of the GEM model is similar to that of the 2001 version of the ECMWF model. The model appears to simulate with realism the large-scale rainy systems but with frequent mislocations. Moreover, the model has a tendency to produce intense small-scale precipitating areas that are not observed. The occurrence frequency of cloud and rain is overestimated by the model. Finally, these results are encouraging enough to continue investigating the assimilation of Tbs in cloudy and rainy skies at MSC.

The 1979–2005 Greenland ice sheet melt extent from passive microwave data using an improved version of the melt retrieval XPGR algorithm

Analysis of passive microwave satellite observations over the Greenland ice sheet reveals a significant increase in surface melt over the period 1979–2005. Since 1979, the total melt area was found to have increased by +1.22 × 107 km2. An improved version of the cross‐polarized gradient ratio (XPGR) technique is used to identify the melt from the brightness temperatures. The improvements in the melt retrieval XPGR algorithm as well as the surface melt acceleration are discussed with results from a coupled atmosphere‐snow regional climate model. From 1979 to 2005, the ablation period has been increasing everywhere over the melt zone except in the regions where the model simulates an increased summer snowfall. Indeed, more snowfall in summer decreases the liquid water content of the snowpack, raises the albedo and therefore reduces the melt. Finally, the observed melt acceleration over the Greenland ice sheet is highly correlated with both Greenland and global warming suggesting a continuing surface melt increase in the future.

Whither Arctic sea ice? A clear signal of decline regionally, seasonally and extending beyond the satellite record

AbstractThe Arctic sea ice has been pointed to as one of the first and clearest indicators of climate change. Satellite passive microwave observations from 1979 through 2005 now indicate a significant –8.4±1.5% decade–1 trend (99% confidence level) in September sea-ice extent, a larger trend than earlier estimates due to acceleration of the decline over the past 41 years. There are differences in regional trends, with some regions more stable than others; not all regional trends are significant. The largest trends tend to occur in months where melt is at or near its peak for a given region. A longer time series of September extents since 1953 was adjusted to correct biases and extended through 2005. The trend from the longer time series is –7.7±0.6% decade–1 (99%), slightly less than from the satellite-derived data that begin in 1979, which is expected given the recent acceleration in the decline.

Snow characterization at a global scale with passive microwave satellite observations

The sensitivity of passive microwave satellite observations to snow characteristics is evaluated, between 19 and 85 GHz, for a winter season, for the Northern Hemisphere. The surface emissivities derived from the Special Sensor Microwave/Imager measurements are systematically compared with in situ snow measurements at 2784 stations, in North America and Eurasia. In addition, coincident satellite responses from active microwave sensors (ERS scatterometer) and visible observations (AVHRR) are also analyzed. Vegetation interferes with the signal that is received by the satellites. Snow emissivities also react to scattering by the snow grain growth that is related to the snow metamorphism during the winter. This phenomenon increases with frequency and is already very sensitive at 37 GHz. Passive microwaves at high frequency (85 GHz) are very sensitive to the presence of snow on the ground, even for very low snow depth. None of the tested satellite measurements is well correlated to the snow depth at a global scale, making snow depth retrieval from these observations very difficult on a global basis. The sensitivity of the satellite observations to snow characteristics depends on local conditions. To partly alleviate these difficulties, a neural network inversion scheme based on local statistics is developed to combine satellite observations, in situ measurements, and land surface model outputs. The combination of different wavelengths partly limits the ambiguities related to the individual sensitivity of each satellite observation to the various sources of variabilities. The final retrieval algorithm is compatible with an assimilation strategy that would better constrain the behavior of surface models. Finally, a clustering algorithm is applied to the suite of satellite observations and clearly shows a strong sensitivity to the snow characteristics and metamorphism during the winter. Characterization of the snowpack using satellite observation classification can yield qualitative information for snow model parameterization.

A model assessment of satellite observed trends in polar sea ice extents

For more than three decades now, satellite passive microwave observations have been used to monitor polar sea ice. Here we utilize sea ice extent trends determined from satellite data for both the Northern and Southern Hemispheres for the period 1972(73)–2004, and assess and interpret them using results from simulations by eleven climate models. In the Northern Hemisphere (NH), observations show a statistically significant decrease of sea ice extent and an acceleration of sea ice retreat during the past three decades. However, from the modeled natural variability of sea ice extents in control simulations, we conclude that the acceleration is not statistically significant and should not be extrapolated into the future. Most of the models, like the observations, show an absence of a prominent seasonal cycle in the trend values. Both observations and model simulations show that climate variability in sea ice extent in the Southern Hemisphere (SH) is much larger than in the NH and that the SH sea ice extent trends are not statistically significant.

Comparison of local scale measured and modelled brightness temperatures and snow parameters from the CLPX 2003 by means of a dense medium radiative transfer theory model

AbstractSome 27 years of passive microwave satellite observations have demonstrated the value of remote sensing for cryospheric applications, albeit at regional to hemispheric scales due to the relatively large sensor footprints (e.g. ∼25 km × 25 km for the advanced microwave scanning radiometer). At the other extreme there is the primarily local‐scale in situ experience of the snow community. Until higher‐resolution sensors become available, understanding the effects of sub‐pixel heterogeneity and the downscaling of observations remains an important topic.In order to provide a baseline for performing scaling studies and for future radiance‐based hydrological assimilation schemes, an evaluation of snowpack forward radiance modelling at the plot scale (least heterogeneous scale) is carried out. Snow microwave radiance models based on dense medium radiative transfer (DMRT) theory incorporate a high degree of physical fidelity, yet dense medium models are particularly sensitive to snowpack structural parameters such as grain size, density, and depth (parameters that may vary substantially within a snowpack). The DMRT performance is evaluated through the use of a fitting technique with respect to snow grain size, a hydrologically important parameter related to the total amount of melt water stored in a snowpack, snowpack metamorphism, vertical energy transport and melt dynamics, as well as various quantities useful in surface energy balance studies. All remaining snow input parameters to the model are derived from snow pit measurements. Model‐predicted radiances are evaluated using microwave brightness measurements collected during the NASA Cold Land Process Experiment (CLPX) by the University of Tokyo's Ground‐Based Microwave Radiometer‐7 (GBMR‐7) system at 18·7, 36·5, and 89 GHz, with incidence angles ranging from 30 to 70 °. The detailed CLPX microwave and snow data provide an excellent opportunity to gain insight into the strengths and weaknesses of this approach. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Ross Sea Ice Motion, Area Flux, and Deformation

Abstract The sea ice motion, area export, and deformation of the Ross Sea ice cover are examined with satellite passive microwave and RADARSAT observations. The record of high-resolution synthetic aperture radar (SAR) data, from 1998 and 2000, allows the estimation of the variability of ice deformation at the small scale (∼10 km) and to assess the quality of the longer record of passive microwave ice motion. Daily and subdaily deformation fields and RADARSAT imagery highlight the variability of motion and deformation in the Ross Sea. With the passive microwave ice motion, the area export at a flux gate positioned between Cape Adare and Land Bay is estimated. Between 1992 and 2003, a positive trend can be seen in the winter (March–November) ice area flux that has a mean of 990 × 103 km2 and ranges from a low of 600 × 103 km2 in 1992 to a peak of 1600 × 103 km2 in 2001. In the mean, the southern Ross Sea produces almost twice its own area of sea ice during the winter. Cross-gate sea level pressure (SLP) gradients explain ∼60% of the variance in the ice area flux. A positive trend in this gradient, from reanalysis products, suggests a “spinup” of the Ross Sea Gyre over the past 12 yr. In both the NCEP–NCAR and ERA-40 surface pressure fields, longer-term trends in this gradient and mean SLP between 1979 and 2002 are explored along with positive anomalies in the monthly cross-gate SLP gradient associated with the positive phase of the Southern Hemisphere annular mode and the extrapolar Southern Oscillation.

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

Abstract. Soil moisture is an important component of the hydrological cycle. In the framework of modern flood warning systems, the knowledge of soil moisture is crucial, due to the influence on the soil response in terms of infiltration-runoff. Precipitation-runoff processes, in fact, are related to catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions is of significant importance to improve the reliability of flood warning systems. Combining such information with other weather-related satellite products (i.e. rain rate estimation) might represent a useful exercise in order to improve our capability to handle (and possibly mitigate or prevent) hydro-geological hazards. Remote sensing, in the last few years, has supported several techniques for soil moisture/wetness monitoring. Most of the satellite-based techniques use microwave data, thanks to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil. On the other hand, microwave data are unfortunately highly affected by the presence of surface roughness or vegetation coverage within the instantaneous satellite field of view (IFOV). Those problems, consequently, strongly limit the efficiency and the reliability of traditional satellite techniques. Recently, using data coming from AMSU (Advanced Microwave Sounding Unit), flying aboard NOAA (National Oceanic and Atmospheric Administration) satellites, a new methodology for soil wetness estimation has been proposed. The proposed index, called Soil Wetness Variation Index (SWVI), developed by a multi-temporal analysis of AMSU records, seems able to reduce the problems related to vegetation and/or roughness effects. Such an approach has been tested, with promising results, on the analysis of some flooding events which occurred in Europe in the past. In this study, results achieved for the HYDROPTIMET test cases will be analysed and discussed in detail. This analysis allows us to evaluate the reliability and the efficiency of the proposed technique in identifying different amounts of soil wetness variations in different observational conditions. In particular, the proposed indicator was able to document the actual effects of meteorological events, in terms of space-time evolution of soil wetness changes, for all the analysed HYDROPTIMET test cases. Moreover, in some circumstances, the SWVI was able to identify the presence of a sort of "early" signal in terms of soil wetness variations, which may be regarded as a timely indication of an anomalous value of soil water content. This evidence suggests the opportunity to use such an index in the pre-operational phases of the modern flood warning systems, in order to improve their forecast capabilities and their reliability.

Tracking the Arctic's shrinking ice cover: Another extreme September minimum in 2004

Satellite passive microwave observations document an overall downward trend in Arctic sea ice extent and area since 1978. While the record minimum observed in September 2002 strongly reinforced this downward trend, extreme ice minima were again observed in 2003 and 2004. Although having three extreme minimum years in a row is unprecedented in the satellite record, attributing these recent trends and extremes to greenhouse gas loading must be tempered by recognition that the sea ice cover is variable from year to year in response to wind, temperature and oceanic forcings.

Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models. This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990–1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.