A Midlatitude Precipitating Cloud Database Validated with Satellite Observations

Vertically integrated cloud liquid water has been successfully analysed from both the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU). However microwave radiometers provide very little information on the vertical cloud liquid water profile. When considering the assimilation of microwave sounding channels, such as those on the AMSU or the Special Sensor Microwave Imager Sounder (SSMIS) the sensitivity of the measurements to cloud liquid water is dependent on the altitude of the cloud. Cloud well below the peak of the weighting function will tend to increase the measured brightness temperatures whereas cloud at or above the weighting function peak will tend to lower the measured brightness temperatures. Therefore if temperature and humidity sounding data are to be assimilated where cloud is present it will be necessary to analyse simultaneously the temperature, humidity and cloud profiles. One approach is to use a one dimensional variational analysis (1D-var) with a single moisture control variable, total water, where total water is the sum of water vapour, cloud liquid water, cloud ice water, rain and snow. For simplicity in this study rain and snow are neglected and ice cloud is assumed to be transparent. The analysis of cloud from the total water approach is compared with a variational analysis with separate cloud and water vapour control variables. The suitability of a total water control variable for variational analysis of cloudy AMSU, SSM/I or SSMIS data is then discussed.

The microwave radiometers on altimeter missions are specified to provide the wet troposphere path delay with an uncertainty of 1 cm or lower, at the location of the altimeter footprint. The constraints on the calibration and stability of these instruments are therefore particularly stringent. The paper addresses the questions of long-term stability and absolute calibration of the National Aeronautics and Space Administration Topography Experiment (TOPEX) and European Space Agency European Remote Sensing 2 (ERS-2) radiometers over the entire range of brightness temperatures. Selecting the coldest measurements over from the two radiometers, the drift of the TOPEX radiometer 18-GHz channel is confirmed to be about 0.2 K/year over the seven first years of the mission, and the one of the ERS-2 radiometer 23.8-GHz channel to be -0.2 K/year. The good stability of the other channels is confirmed (drift less than 0.04 K/year). The use of continental targets for analyzing the long-term drift is evaluated: the natural interannual variability prevents one from directly monitoring the drift of each channel, but the relative variation between two channels of the same instrument is found reliable. Over areas (Antarctic and Greenland plateau), results are consistent with the cold ocean analysis. Intercomparison of radiometer absolute calibrations is performed over the same continental area, leading to an anomalously high difference between channels 36.5 and 37 GHz of the ERS-2 and TOPEX radiometers, respectively, over hot targets (Sahara desert and Amazon forest). To quantify and analyze this difference, other radiometer measurements are analyzed over the Amazon forest, from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU). Biases are confirmed for both TOPEX and ERS-2 radiometers by comparing brightness temperatures and derived surface emissivities: the TOPEX radiometer channels exhibit a negative bias with respect to SSM/I and AMSU-A, whereas the ERS-2 radiometer 36.5-GHz channel is positively biased, by several kelvin in brightness temperature in both cases. The method presented here could be used for controlling the in-flight calibration of any radiometer, and correct for remaining calibration errors after launch.

This paper develops a neural network based precipitation retrieval algorithm called the PSU Infrared Precipitation retrieval algorithm version 1 (PIP-1), which employs infrared observations from the Geostationary Operational Environmental Satellite (GOES)-12 Imager. Neural networks are trained and evaluated using overlapping surface precipitation rates from the AMSU MIT Precipitation retrieval (AMP) products accurately retrieved using observations from the passive millimeter-wave spectrometer Advanced Microwave Sounding Unit (AMSU) aboard the U.S. National Oceanic and Atmospheric Administration (NOAA)-18 satellite. Results show good agreement between PIP-1 retrievals and AMP surface precipitation rates in terms of rates, positions, and morphology. PIP-1 retrievals are useful at rates higher than 1 mm/h. Employing PIP-1 with observations from GOES Imagers can provide useful surface precipitation retrievals in real time at every half an hour. PIP-1 can be adapted to work with other geostationary infrared satellites with similar channel characteristics to those of GOES Imagers.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

ABSTRACTA sea ice thermal microwave emission model for 50 GHz was developed under EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSI SAF) programme. The model is based on correlations between the surface brightness temperature at 18, 36 and 50 GHz. The model coefficients are estimated using simulated data from a combined thermodynamic and emission model. The intention with the model is to provide a first guess sea ice surface emissivity estimate for atmospheric temperature sounding applications in the troposphere in numerical weather prediction (NWP) models assimilating Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave Imager/Sounder (SSMIS) data. The spectral gradient ratio is defined as the difference over the sum of the SSMIS brightness temperatures at 18 and 36 GHz vertical linear polarisation (GR1836). The GR1836 is related to the emissivity at the atmospheric temperature sounding channels at around 50 GHz. Furthermore, the brightness temperatures and the polarisation ratio (PR) at the neighbouring 18, 36 and 50 GHz channels are highly correlated. Both the gradient ratio at 18 and 36 GHz and the PR at 36 GHz measured by SSMIS are input into the model predicting the 50 GHz emissivity for horizontal and vertical linear polarisations and incidence angles between 0° and 60° The simulated emissivity is compared to the emissivity derived with alternative methods. The fit to real AMSU observations is investigated using the different emissivity estimates for simulating the observations with atmospheric data from a regional weather prediction model.

Assimilation of SSM/I-retrieved rainrate has been shown to be beneficial in the numerical prediction of tropical cyclones with a limited-area model (Peng and Chang, 1996). However, due to the near sun-synchronous orbits and the narrow swath, SSM/I coverage of the tropics is not complete. There are many instances that one or more tropical cyclones are not covered by SSM/I swaths; therefore, rainrates cannot be retrieved for assimilation into the prediction models. In this study, the effect of the assimilation of rainrate inferred from cloud top brightness temperature (BT) is assessed. The algorithm inferring rainrates from infrared (IR) cloud top brightness temperature based on concurrent retrieval of the Special Sensor Microwave/Imager (SSM/I) rainfall of Manobianco et al. (1994) was applied in a numerical prediction experiment for Typhoon Flo of 1990. It was found that assimilating IR rainfall rates produced a positive impact on both track and intensity forecasts of Typhoon Flo, qualitatively similar to those in Peng and Chang using the SSM/I-retrieved rainrates. The analytic algorithm relating the IR rainrates to the SSM/I rainrates became less accurate when concurrent IR and SSM/I brightness temperatures were not available. The accuracy of the IR rainrates and improvements in forecast skill both decreased in a sensitivity experiment where IR rainrates were obtained by an obsolete relationship of IR brightness temperature and rainfall rates.

Brightness temperature spectra measured by the Special Sensor Microwave/Imager (SSM/I) flown onboard F8 and F14 satellites of the U.S. Defense Meteorological Satellite Program (DMSP) during the 1987–1988 and 1997–1998 winter periods are analyzed concurrently with the data from snow monitoring stations over the former Soviet Union. Extensive analysis reveals the existence of two anomalies in the microwave thermal radiation spectra of snow cover. It is shown that in the beginning of winter the SSM/I measurements at 19, 37, and 85 GHz generally follow a classical pattern; that is, the brightness temperatures decrease for both increasing snow depth and increasing frequency. Dramatic departures from this behavior is observed around the middle of winter: The brightness temperatures reach a minimum and then begin to increase despite the fact that the snow depth remains constant or even continues to grow. Statistical analysis of the snow pack characteristics and SSM/I measurements is presented around the time when the brightness temperatures reach a minimum. The anomalous spectral characteristics are analyzed using a two‐stream radiative transfer model and dense media theory. It is shown how metamorphic changes in the snow crystalline structure are responsible for the brightness temperature minimum. The second departure from the normal snow signature is the inversion of brightness temperature spectra; that is, the higher‐frequency brightness temperature is greater than the low‐frequency measurements. It is shown that this phenomenon, observed previously over Greenland and Antarctica, is much more extensive. Radiative transfer simulations were used to show that a dense layer of surface crust on top of old coarse‐grained snow can produce the invented brightness temperature spectrum.

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records.

Total precipitable water (TPW), the amount of water vapor in a column from the surface of the earth to space, is used by forecasters to predict heavy precipitation. In this paper, a process for blending TPW values retrieved from two satellite sources is detailed: the Advanced Microwave Sounding Unit (AMSU) instruments on three NOAA satellites, and the Special Sensor Microwave Imager (SSM/I) instruments on three Defense Meteorological Satellite Program (DMSP) satellites. The process starts with a blending algorithm, which matches the cumulative probability distribution functions of TPW retrievals from the two instruments to lessen their differences. The data are then mapped to a map projection useful to forecasters and composited for 12 h to make a global map. These maps are produced hourly using Data Processing and Error Analysis System (DPEAS) software and made available to forecasters online.

This study attempts to exploit ‘Satellite with Advanced Research and Global Observation Satellite (Argos) and Ka-band Altimeter (ALtiKa)’ (SARAL/AltiKa) and Passive Microwave Radiometer (PMR) measurements for the retrieval of precipitation and rain rate (R Rain) over the global oceans. The altimetric measurements are affected by the presence of rain and these measurements are thus required to be identified. In this research, the concurrent SARAL and Tropical Rainfall Measuring Mission – Precipitation Radar (TRMM-PR) measurements are used to show the rain sensitivity of some of the altimeter derived parameters and PMR-derived Brightness Temperature. Based on the probability distribution of rain-sensitive parameters, a probabilistic rain identification algorithm is proposed, which is optimized to reduce the false-alarm cases for rain estimation. In the second step, a Genetic Algorithm (GA)-based rain measurement algorithm is developed. The algorithm is applied to a full year (2016) of independent SARAL measurements, compared with collocated Global Precipitation Measurement (GPM) Mission Microwave Imager (GMI) and Special Sensor Microwave Imager Sounder (SSMIS) measurements. Within TRMM latitudinal coverage area (i.e. < ± 40° latitudes), the comparison with GMI and SSMIS shows correlation coefficient (r) of 0.78 and 0.76 and root mean square difference (RMSD) of 0.48 and 0.43 (mm h−1), respectively. The comparison, beyond latitudinal coverage of TRMM-PR (i.e. outside ± 40° latitudes), with GMI and SSMIS, shows r of 0.63 and 0.60 and RMSD of 0.59 and 0.55 (mm h−1), respectively. The algorithm is further applied to measure rain associated with a tropical cyclone. These rain measurements are then compared with near-concurrent rain measurements from SSMIS, and comparison results are presented in the paper.

Abstract. Firn density plays a crucial role in assessing the surface mass balance of the Antarctic ice sheet. However, our understanding of the spatial and temporal variations in firn density is limited due to (i) spatial and temporal limitations of in situ measurements, (ii) potential modelling uncertainties, and (iii) lack of firn density products driven by satellite remote-sensing data. To address this gap, this paper explores the potential of satellite microwave radiometer (Special Sensor Microwave Imager/Sounder (SSMIS)) and scatterometer (Advanced Scatterometer (ASCAT)) observations for assessing spatial and temporal dynamics of dry-firn density over the Antarctic ice sheet. Our analysis demonstrates a clear relation between density anomalies at a depth of 40 cm and fluctuations in satellite observations. However, a linear relationship with individual satellite observations is insufficient to explain the spatial and temporal variation in snow density. Hence, we investigate the potential of a non-linear random forest (RF) machine learning approach trained on radiometer and scatterometer data to derive the spatial and temporal variations in dry-firn density. In the estimation process, 10 years of SSMIS observations (brightness temperature) and ASCAT observations (backscatter intensity) is used as input features to a random forest (RF) regressor. The regressor is first trained on time series of modelled density and satellite observations at randomly sampled pixels and then applied to estimate densities in dry-firn areas across Antarctica. The RF results reveal a strong agreement between the spatial patterns estimated by the RF regressor and the modelled densities. The estimated densities exhibit an error of ±10 kg m−3 in the interior of the ice sheet and ±35 kg m−3 towards the ocean. However, the temporal patterns show some discrepancies, as the RF regressor tends to overestimate summer densities, except for high-elevation regions in East Antarctica and specific areas in West Antarctica. These errors may be attributed to underestimations of short-term or seasonal variations in the modelled density and the limitations of RF in extrapolating values outside the training data. Overall, our study presents a potential method for estimating unknown Antarctic firn densities using known densities and satellite parameters.

As part of a calibration/validation effort for the Special Sensor Microwave/Imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85 V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

&lt;p&gt;為了討論快速增強(Rapid Intensification; RI)和緩慢增強(Slow Intensification; SI)颱風之間的降雨類型和雲微物理特徵的差異此，我們分析SSMIS (Special Sensor Microwave Imager/Sounder)衛星酬載儀器反演的冰水路徑(Ice Water Path; IWP)、雨水路徑(Rain Water Path; RWP)及紅外頻道的亮度溫度(Brightness Temperature)，並進行WRF (Weather Research and Forecasting)模式的數值模擬，以討論RI和SI颱風間的降雨類型和雲微物理特徵之差異與其演變比較。&lt;/p&gt; &lt;p&gt;從SSMIS觀測資料中，我們發現在2倍最大風速半徑（RMW）範圍內，RI颱風的IWP與RWP量值在增強開始前12小時後會大於SI颱風的對應量值。且在增強開始前12小時和增強開始後的12及24小時，IWP和RWP量值在2倍RMW範圍內，RI颱風的IWP/RWP量值顯著多於SI颱風的量值，而且此差異達到95%信心水準。紅外頻道的亮溫資料指出，在增強前12小時，颱風3倍RMW內的區域之RI颱風個案的亮溫低於SI颱風個案，並於增強開始後2倍RMW內，多數區域RI颱風紅外的亮溫顯著低於SI颱風的量值(達到95%信心水準)，尤其是在上風切處格外顯著。&lt;/p&gt; &lt;p&gt;在SSMIS衛星觀測與WRF模擬結果中，我們發現於颱風增強開始前，IWP和RWP在下風切處的量值大於上風切處的量值，其中又以Downshear Left (DL)象限的量值最大。在RI開始後，Upshear Left (UL)象限中的IWP和RWP逐漸增大，但是SI的颱風並沒有出現類似的演變。同時我們發現，RI颱風個案在其RI正要開始前，IWP與RWP及對流有發生快速減弱的情形。在WRF控制組模擬RI和SI颱風個案的降水類型差異比較中，我們發現在進行分析的大多數時段中，RI 颱風個案中DL和UL象限的深對流（Deep Convection; DC）和中等對流（Moderate Convection; MC）的比率大於SI 颱風個案的比率。此外，不論RI或SI的颱風個案其上風切處的層狀降水(Stratiform; ST)面積，均在增強(RI或SI)開始的前後時間有逐漸增加的情形。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;The differences and evolution of rainfall regimes and microphysics characteristics between rapid-intensification (RI) and slow-intensification (SI) tropical cyclones (TCs) are examined in this study. We have analyzed the ice water path (IWP) and rain water path (RWP) retrieved from the Special Sensor Microwave Imager/Sounder (SSMIS), brightness temperaute from infrared channel, and conducted the Weather Research and Forecasting model (WRF) simulations to investigate the differences and evolution of rainfall regimes and microphysics characteristics between RI and SI TCs.&lt;/p&gt; &lt;p&gt;From the SSMIS observations, we found that the IWP and RWP in RI TCs were greater than those in SI TCs within twice the radius of maximum wind speed (RMW) 12 hours before the RI onset. The differences in IWP and RWP between RI and SI TCs within the RMW were significant at the 95% confidence level at the RI/SI onset and within twice the RMW at 12 hours before, 12 hours after, and 24 hours after the onset. The brightness temperature from infrared channel indicated that, at 12 hours before onset, the brightness temperature of the RI cases was lower than that of the SI cases within 3 times of RMW. After the onset, the RI cases exhibited significantly lower infrared brightness temperatures compared to the SI cases at the 95% confidence level within most areas within twice RMW. This difference was particularly evident in the upshear quadrant.&lt;/p&gt; &lt;p&gt;Both the SSMIS observations and WRF simulations showed that the IWP and RWP were generally larger in the downshear flank before the onset, especially in the downshear left (DL) quadrant. The IWP and RWP in upshear left (UL) quadrant gradually became larger after the RI onset, yet similar evolution did not occur in SI TCs. The IWP and RWP were quickly weakened just before the RI onset in RI TCs.&lt;/p&gt; &lt;p&gt;We also compared the precipitation regime differences between the RI and SI TCs. We found that the ratios of deep convection (DC) and moderate convection (MC) at the DL and UL quadrants in RI TCs were greater than those in SI TCs for most periods we analyzed. The ratios of stratiform precipitation (ST) at the UL and upshear right (UR) quadrants gradually increased after the RI/SI onset.&lt;/p&gt; &lt;p&gt; &lt;/p&gt;

Two calibration anomalies of the Defense Meteorological Satellite Program's (DMSP) Special Sensor Microwave Imager Sounder (SSMIS) radiometer are examined by using several sources of data. Early orbit mode data from the SSMIS are used to create radiometric images of the warm calibration load that evolve over an entire orbit to elucidate the effects of direct and reflected solar illumination of the warm-load (WL) emissive surface. Analysis of the radiometric gain and apparent WL radiometric brightness temperature observed during the solar intrusion events show the impact of these events on the SSMIS calibration. A graphical simulation of the SSMIS and DMSP spacecraft is used to define the regions where solar intrusion occurs and to characterize the WL anomalous regions for the specific DMSP F-16 orbit. The graphical simulation is also used to determine the cause of additional calibration errors that were identified by using comparisons to numerical weather prediction (NWP) models, as emission from the SSMIS reflector antenna. Mitigation of these calibration anomalies is critical if the operational SSMIS radiometers achieve their full utility in NWP, climate monitoring, forecasting, and other emerging applications. A detailed characterization of the SSMIS calibration provides a basis for this process.

Over oceans, total column water vapour (TCWV) estimates derived from brightness temperatures of the Special Sensor Microwave Imager (SSM/I) have been shown to give accuracies similar to radiosonde measurements. Data provided by the SSM/I on the DMSP satellites are not available in real‐time, and so they are not assimilated into the ECMWF model. They can thus be used for independent validation of this important aspect of the model's hydrological cycle. The quality of ECMWF operational analyses of humidity is assessed for the period between August 1992 and November 1994. At that time, assimilation was performed through an Optimal Interpolation scheme. For this work, 2‐week averages of TCWV have been computed from analysis humidity fields of the ECMWF model and compared with simultaneous SSM/I estimates. Discrepancies between model and SSM/I estimates associated with different meteorological regimes are discussed. According to the season, the underestimation of analysed humidity in the tropics can reach 30 to 50%, whereas oceanic subsidence areas located off western coasts of subtropical continents appear too wet by as much as 60%. Mid‐latitudes are generally too moist, but to a lesser extent. An example is also shown of the utilization of SSM/I TCWV to evaluate the impact of an experimental prognostic cloud scheme on analyses and forecasts.