Strengths and limitations of MST radar measurements of middle-atmosphere winds

Radars have been used successfully for many years to measure atmospheric motions over a wide range of altitudes, from ground level up to heights of several hundred kilometres into the ionosphere. In this paper we particularly wish to concentrate on the accuracy of these measurements for winds in the middle atmosphere (i.e. 10–100–km altitude). We begin by briefly reviewing the literature relating to comparisons between radar methods and other techniques. We demonstrate where the radar data are most and least reliable and then, in parallel with a discussion about the basic principles of the method, discuss why these different regimes have the different accuracies and precisions they do. This discussion is used to highlight the strengths and weaknesses of radar methods. Issues like radar volume, aspect sensitivity, gravity wave effects and scatterer intermittency in producing wind biases, and the degree by which the intermittent generation of scatterers at quasi-random points in space could skew the radar measurements, are all considered. We also investigate the possibility that MF radar techniques can be contaminated by E-region scatter to heights as low as 92–95–km altitude (i.e. up to 8–10 km below the ionospheric peak echo). Within all these comments, however, we also recognize that radar methods still represent powerful techniques which have an important future at all levels of the atmosphere.     

Use of Scale Recursive Estimation for assimilation of precipitation data from TRMM (PR and TMI) and NEXRAD

The paper shows an application of Scale Recursive Estimation (SRE) used to assimilate rainfall rates estimated during a storm event from three remote sensing devices. These are the TMI radiometer and the PR radar, carried on board of the TRMM satellite and the KNQA Memphis Weather Surveillance radar, belonging to the NEXRAD network, each one providing rain rate estimates at a different spatial scale. The variability of rain rate process in scales is modeled as a multiplicative random cascade, including spatial intermittence. The observational noise in the estimates is modeled according to a multiplicative error. System estimation, including process and observational noise, is carried out using Maximum Likelihood Estimation implemented by a scale recursive Expectation Maximization (EM) algorithm. As a result, new rainfall rate estimates are obtained that feature decreased estimation error as compared to those coming from each device alone. The performance of the SRE-EM approach is compared with that of the latest methods proposed for data fusion of multisensor estimates. The proposed approach improves the current methods adopted for SRE and provides an alternative for data fusion in the field of precipitation.     

A statistical study of underestimates of wind speeds by VHF radar

Comparisons are made between horizontal wind measurements carried out using a VHF-radar system at Aberystwyth (52.4°N, 4.1°W) and radiosondes launched from Aberporth, some 50 km to the southwest. The radar wind results are derived from Doppler wind measurements at zenith angles of 6° in two orthogonal planes and in the vertical direction. Measurements on a total of 398 days over a 2-year period are considered, but the major part of the study involves a statistical analysis of data collected during 75 radiosonde flights selected to minimise the spatial separation of the two sets of measurements. Whereas good agreement is found between the two sets of wind direction, radar-derived wind speeds show underestimates of 4–6% compared with radiosonde values over the height range 4–14 km. Studies of the characteristics of this discrepancy in wind speeds have concentrated on its directional dependence, the effects of the spatial separation of the two sets of measurements, and the influence of any uncertainty in the radar measurements of vertical velocities. The aspect sensitivity of radar echoes has previously been suggested as a cause of underestimates of wind speeds by VHF radar. The present statistical treatment and case-studies show that an appropriate correction can be applied using estimates of the effective radar beam angle derived from a comparison of echo powers at zenith angles of 4.2° and 8.5°.     

Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty

Radar estimates of rainfall are being increasingly applied to flood forecasting applications. Errors are inherent both in the process of estimating rainfall from radar and in the modelling of the rainfall–runoff transformation. The study aims at building a framework for the assessment of uncertainty that is consistent with the limitations of the model and data available and that allows a direct quantitative comparison between model predictions obtained by using radar and raingauge rainfall inputs. The study uses radar data from a mountainous region in northern Italy where complex topography amplifies radar errors due to radar beam occlusion and variability of precipitation with height. These errors, together with other error sources, are adjusted by applying a radar rainfall estimation algorithm. Radar rainfall estimates, adjusted and not, are used as an input to TOPMODEL for flood simulation over the Posina catchment (116 km²). Hydrological model parameter uncertainty is explicitly accounted for by use of the GLUE (Generalized Likelihood Uncertainty Estimation). Statistics are proposed to evaluate both the wideness of the uncertainty limits and the percentage of observations which fall within the uncertainty bounds. Results show the critical importance of proper adjustment of radar estimates and the use of radar estimates as close to ground as possible. Uncertainties affecting runoff predictions from adjusted radar data are close to those obtained by using a dense raingauge network, at least for the lowest radar observations available. Copyright © 2004 John Wiley & Sons, Ltd.     

Comparison of methods for estimating earth resistivity from airborne electromagnetic measurements

Earth resistivity estimates from frequency domain airborne electromagnetic data can vary over more than two orders of magnitude depending on the half-space estimation method used. Lookup tables are fast methods for estimating half-space resistivities, and can be based on in-phase and quadrature measurements for a specified frequency, or on quadrature and sensor height. Inverse methods are slower, but allow sensor height to be incorporated more directly. Extreme topographic relief can affect estimates from each of the methods, particularly if the portion of the line over the topographic feature is not at a constant height above ground level. Quadrature–sensor height lookup table estimates are generally too low over narrow valleys. The other methods are also affected, but behave less predictably. Over very good conductors, quadrature–sensor height tables can yield resistivity estimates that are too high. In-phase–quadrature tables and inverse methods yield resistivity estimates that are too high when the earth has high magnetic susceptibility, whereas quadrature–sensor height methods are unaffected. However, it is possible to incorporate magnetic susceptibility into the in-phase–quadrature lookup table. In-phase–quadrature lookup tables can give different results according to whether the tables are ordered according to the in-phase component or the quadrature component. The rules for handling negative in-phase measurements are particularly critical. Although resistivity maps produced from the different methods tend to be similar, details can vary considerably, calling into question the ability to make detailed interpretations based on half-space models.     

Zonal mean and tidal dynamics from space: an empirical examination of aliasing and sampling issues

Interpretations of space-based measurements of atmospheric parameters in the mesosphere and thermosphere are complicated by large local-time variations at these altitudes. For this reason, satellite orbits are often preferred which precess through all local times one or more times per season. However, the local-time structure of the atmosphere is inherently non-stationary, which can lead to sampling and aliasing difficulties when attempting to deconvolve the measurements into zonal mean and tidal components. In the present study, hourly radar measurements of mesopause-region winds are used to form a mock data base which can be used to gain insight into implications of the aforementioned problems; the use of actual measurements introduces a realistic element of geophysical temporal variability. Assuming zonal symmetry (i.e., migrating tides superimposed on a zonal mean circulation), the radar measurements are sampled from the satellite perspective for orbital inclinations of 57° and 70°, and compared to the ground or true perspective. These comparisons provide realistic estimates of the errors to be expected when attempting to derive mean and tidal components from space-based measurements. For both diurnal and semidiurnal components, and the quoted satellite inclinations, acceptable errors (3–4 m/s rms) are obtained for data covering 24 h local time (i.e., ascending plus descending nodes); the corresponding errors for singlenode data (12 h local-time coverage) are of order 8–11 m/s, and therefore may not represent reliable estimates of the actual tidal components. There exist certain caveats in connection with the latter conclusion which are discussed.     

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.     

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.     

Dependence of radar quantitative precipitation estimation error on the rain intensity in the Cévennes region,France

Abstract

Radar quantitative precipitation estimates (QPEs) were assessed using reference values established by means of a geostatistical approach. The reference values were estimated from raingauge data using the block kriging technique, and the reference meshes were selected on the basis of the kriging estimation variance. Agreement between radar QPEs and reference rain amounts was shown to increase slightly with the space–time scales. The statistical distributions of the errors were modelled conditionally with respect to several factors using the GAMLSS approach. The conditional bias of the errors presents a complex structure that depends on the space–time scales and the considered geographical sub-domains, while the standard deviation of the errors has a more homogeneous behaviour. The estimation standard deviation of the reference rainfall and the standard deviation of the errors between radar and reference rainfall were found to have the same magnitude, indicating the limitations of the available network in terms of providing accurate reference values for the spatial scales considered (5–100 km²).

Editor D. Koutsoyiannis; Guest editor R.J. Moore

Citation Delrieu, G., Bonnifait, L., Kirstetter, P.-E., and Boudevillain, B., 2013. Dependence of radar quantitative precipitation estimation error on the rain intensity in the Cévennes region, France. Hydrological Sciences Journal, 59 (7), 1300–1311. http://dx.doi.org/10.1080/02626667.2013.827337     

A multi‐source precipitation estimation approach to fill gaps over a radar precipitation field: a case study in the Colorado River Basin

The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground‐based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses an ensemble‐based method that aims to estimate spatially varying multiplicative biases in SPEs using a radar precipitation product. A weighted successive correction method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial model for merging the rain gauges (RGs) and climatological precipitation sources with radar and SPEs. We demonstrated the method using a satellite‐based hydro‐estimator; a radar‐based, stage‐II; a climatological product, Parameter‐elevation Regressions on Independent Slopes Model and a RG dataset for several rain events from 2006 to 2008 over an artificial gap in Oklahoma and a real radar gap in the Colorado River basin. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, RG, Parameter‐elevation Regressions on Independent Slopes Model and satellite products, a radar‐like product is achievable over radar gap areas that benefit the operational meteorology and hydrology community. Copyright © 2013 John Wiley & Sons, Ltd.     

Radar rainfall estimation for flash flood forecasting in small urban watersheds

Radar rainfall estimation for flash flood forecasting in small, urban catchments is examined through analyses of radar, rain gage and discharge observations from the 14.3 km² Dead Run drainage basin in Baltimore County, Maryland. The flash flood forecasting problem pushes the envelope of rainfall estimation to time and space scales that are commensurate with the scales at which the fundamental governing laws of land surface processes are derived. Analyses of radar rainfall estimates are based on volume scan WSR-88D reflectivity observations for 36 storms during the period 2003–2005. Gage-radar analyses show large spatial variability of storm total rainfall over the 14.3 km² basin for flash flood producing storms. The ability to capture the detailed spatial variation of rainfall for flash flood producing storms by WSR-88D rainfall estimates varies markedly from event to event. As spatial scale decreases from the 14.3 km² scale of the Dead Run watershed to 1 km² (and the characteristic time scale of flash flood producing rainfall decreases from 1 h to 15 min) the predictability of flash flood response from WSR-88D rainfall estimates decreases sharply. Storm to storm variability of multiplicative bias in storm total rainfall estimates is a dominant element of the error structure of radar rainfall estimates, and it varies systematically over the warm season and with flood magnitude. Analyses of the 7 July 2004 and 28 June 2005 storms illustrate microphysical and dynamical controls on radar estimation error for extreme flash flood producing storms.     

Rainfall estimation in the Sahel: the EPSAT-NIGER experiment

Abstract

EPSAT-NIGER (Estimation of Precipitation by SATellite—NIGER experiment) has been designed to improve the understanding of the precipitation systems of Sudano-Sahelian Africa and to develop operational rainfall estimation algorithms for this region. It is based on the combined use of a very dense raingauge network (93 gauges over a study area of 16 000 km₂) and a C-band weather radar system. The experiment is scheduled to last three years, 1990–1992. The network pattern, a regular grid with nodes spaced at 12.5 km and a 16 gauge target area where the distance between stations is decreased to 1 km, has allowed for some preliminary studies on the rainfall distribution at various space and time scales. Whereas the long term average rainfall gradient is uniform, rainfall increasing north to south, a single rainy season can be markedly different. The local variability may be extremely large. That variability is enhanced at smaller sampling time steps and the computation of reference areal rainfall for satellite imagery validation is extremely sensitive to the design of the ground-based validation system. The joint processing of gauge and radar data has led to the identification of a few typical features of the drop size distribution of the African squall lines, which could lead to deriving specific algorithms for radar calibration in this region. The data provided by EPSAT-NIGER will be used in various international projects for the assessment of water input from the atmosphere to the continent over the Sahel.     

Ground truthed performance of single‐and dual‐polarized radar rain rates at large ranges

Radar accuracy in estimating qualitative precipitation estimation at distances larger than 120 km degrades rapidly because of increased volume coverage and beam height. The performance of the recently upgraded dual‐polarized technology to the NEXRAD network and its capabilities are in need of further examination, as improved rainfall estimates at large distances would allow for significant hydrological modelling improvements. Parameter based methods were applied to radars from St. Louis (KLSX) and Kansas City (KEAX), Missouri, USA, to test the precision and accuracy of both dual‐ and single‐polarized parameter estimations of precipitation at large distances. Hourly aggregated precipitation data from terrestrial‐based tipping buckets provided ground‐truthed reference data. For all KLSX data tested, an R(Z,ZDR) algorithm provided the smallest absolute error (3.7 mm h^?1) and root‐mean‐square‐error (45%) values. For most KEAX data, R(ZDR,KDP) and R(KDP) algorithms performed best, with RMSE values of 37%. With approximately 100 h of precipitation data between April and October of 2014, nearly 800 and 400 mm of precipitation were estimated by radar precipitation algorithms but was not observed by terrestrial‐based precipitation gauges for KLSX and KEAX, respectively. Additionally, nearly 30 and 190 mm of measured precipitation observed by gauges were not detected by the radar rainfall estimates from KLSX and KEAX, respectively. Results improve understanding of radar based precipitation estimates from long ranges thereby advancing applications for hydrometeorological modelling and flood forecasting. Copyright © 2016 John Wiley & Sons, Ltd.     

The Use of Radar and Gauge Measurements to Estimate Areal Precipitation for Several Czech River Basins

Two methods estimating areal precipitation for selected river basins in the Czech Republic are compared. The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute. Data from a dense network of climatological rain gauges (the average inter-station distance is approximately 8 km), the measurements of which are not available in real time, are utilized for the verification. The mean areal precipitation, which is used as the ground truth, is obtained by the weighted interpolation of the dense rain gauge network. The accuracy of the methods is evaluated by the root-mean-square-error.The first, pixel-related method merges radar precipitation with rain gauge data to obtain adjusted pixel values. The adjusting procedure combines radar and gauge values in one variable that is interpolated into all radar pixels. The adjusted pixel precipitation is calculated from radar precipitation and from the value of the combined variable. The areal estimates are determined by adding the corresponding pixel values. The second method applies a linear regression model to describe the relationship between the areal precipitation (dependent variable) and its estimates, which are determined from (i) non-adjusted radar precipitation and (ii) on-line rain gauge measurements interpolated into pixels. Classical linear regression, ridge regression and robust regression models are tested.Both the methods decrease the average areal error in comparison with the reference method, which uses the on-line rain gauge data only. The decrease is about 10% and 15% for the pixel-related and regression methods, respectively. When the estimates of the pixel-related method are included as predictors into the regression method then the improvement of accuracy is almost 25%.     

混合ADI-FDTD亚网格技术在探地雷达频散媒质中的高效正演

提出混合ADI-FDTD亚网格技术开展频散介质GPR正演,即在物性参数变化剧烈局部区域采用细网格剖分ADI-FDTD计算,其他的区域采用粗网格剖分常规FDTD计算,ADI-FDTD突破了CFL条件的限制,可选取与粗网格一致的大时间步长,有效地提高了计算效率.本文首先基于Debye方程,推导了粗网格FDTD及细网格ADI-FDTD频散介质差分格式,着重对粗细两种网格结合的场值交换方式进行了深入探讨,给出了该算法的计算流程.然后以一个薄层模型为例,分别应用粗网格、细网格、混合ADI-FDTD亚网格算法对该模型进行正演,计算资源的占用及模拟精度说明了混合ADI-FDTD亚网格算法的优势.最后,建立频散介质与非频散介质的组合模型,应用3种方法对该模型进行正演,对比3种方法优劣,分析雷达剖面中非频散介质及频散介质中波形特征,有效地指导雷达资料的精确解释.     

The dynamic cusp at low altitudes: a case study utilizing Viking,DMSP-F7, and Sondrestrom incoherent scatter radar observations

Coincident multi-instrument magnetospheric and ionospheric observations have made it possible to determine the position of the ionospheric footprint of the magnetospheric cusp and to monitor its evolution over time. The data used include charged particle and magnetic field measurements from the Earth-orbiting Viking and DMSP-F7 satellites, electric field measurements from Viking, interplanetary magnetic field and plasma data from IMP-8, and Sondrestrom incoherent scatter radar observations of the ionospheric plasma density, temperature, and convection. Viking detected cusp precipitation poleward of 75.5○ invariant latitude. The ionospheric response to the observed electron precipitation was simulated using an auroral model. It predicts enhanced plasma density and elevated electron temperature in the upper E- and F-regions. Sondrestrom radar observations are in agreement with the predictions. The radar detected a cusp signature on each of five consecutive antenna elevation scans covering 1.2 h local time. The cusp appeared to be about 2○ invariant latitude wide, and its ionospheric footprint shifted equatorward by nearly 2○ during this time, possibly influenced by an overall decrease in the IMF B_z component. The radar plasma drift data and the Viking magnetic and electric field data suggest that the cusp was associated with a continuous, rather than a patchy, merging between the IMF and the geomagnetic field.     

Retrieval of volcanic ash particle size, mass and optical depth from a ground-based thermal infrared camera

Volcanoes can emit fine-sized ash particles (1–10 μm radii) into the atmosphere and if they reach the upper troposphere or lower stratosphere, these particles can have deleterious effects on the atmosphere and climate. If they remain within the lowest few kilometers of the atmosphere, the particles can lead to health effects in humans and animals and also affect vegetation. It is therefore of some interest to be able to measure the particle size distribution, mass and other optical properties of fine ash once suspended in the atmosphere. A new imaging camera working in the infrared region between 7–14 μm has been developed to detect and quantify volcanic ash. The camera uses passive infrared radiation measured in up to five spectral channels to discriminate ash from other atmospheric absorbers (e.g. water molecules) and a microphysical ash model is used to invert the measurements into three retrievable quantities: the particle size distribution, the infrared optical depth and the total mass of fine particles. In this study we describe the salient characteristics of the thermal infrared imaging camera and present the first retrievals from field studies at an erupting volcano. An automated ash alarm algorithm has been devised and tested and a quantitative ash retrieval scheme developed to infer particle sizes, infrared optical depths and mass in a developing ash column. The results suggest that the camera is a useful quantitative tool for monitoring volcanic particulates in the size range 1–10 μm and because it can operate during the night, it may be a very useful complement to other instruments (e.g. ultra-violet spectrometers) that only operate during daylight.     

The effects of wind and rainfall on suspended sediment concentration related to the 2004 Indian Ocean tsunami

The effects of rainfall and wind speed on the dynamics of suspended sediment concentration (SSC), during the 2004 Indian Ocean tsunami, were analyzed using spatial statistical models. The results showed a positive effect of wind speed on SSC, and inconsistent effects (positive and negative) of rainfall on SSC. The effects of wind speed and rainfall on SSC weakened immediately around the tsunami, indicating tsunami-caused floods and earthquake-induced shaking may have suddenly disturbed the ocean–atmosphere interaction processes, and thus weakened the effects of wind speed and rainfall on SSC. Wind speed and rainfall increased markedly, and reached their maximum values immediately after the tsunami week. Rainfall at this particular week exceeded twice the average for the same period over the previous 4 years. The tsunami-affected air–sea interactions may have increased both wind speed and rainfall immediately after the tsunami week, which directly lead to the variations in SSC.     

Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin

Abstract

Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales. We examined the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001. Inter-comparison between 8-day average MOD16 ET estimates and flux tower measurements yielded correlations of 0.78 to 0.81, with root mean square errors (RMSE) of 0.78 and 0.46 mm d^-1, at PDG and USE, respectively. At the PDG site, the annual ET estimate derived by the MOD16 algorithm was 19% higher than the measured amount. For the average annual ET at the basin-wide scale (over an area of 145 000 km²), MOD16 estimates were 21% lower than those from the hydrological model MGB-IPH. Misclassification of land use and land cover was identified as the largest contributor to the error from the MOD16 algorithm. These estimates improve significantly when results are integrated into monthly or annual time intervals, suggesting that the algorithm has a potential for spatial and temporal monitoring of the ET process, continuously and systematically, through the use of remote sensing data.

Editor D. Koutsoyiannis; Associate editor T. Wagener

Citation Ruhoff, A.L., Paz, A.R., Aragao, L.E.O.C., Mu, Q., Malhi, Y., Collischonn, W., Rocha, H.R., and Running, S.W., 2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58 (8), 1658–1676.     

Integrating gravimetric and interferometric synthetic aperture radar data for enhancing reservoir history matching of carbonate gas and volatile oil reservoirs

Reservoir history matching is assuming a critical role in understanding reservoir characteristics, tracking water fronts, and forecasting production. While production data have been incorporated for matching reservoir production levels and estimating critical reservoir parameters, the sparse spatial nature of this dataset limits the efficiency of the history matching process. Recently, gravimetry techniques have significantly advanced to the point of providing measurement accuracy in the microgal range and consequently can be used for the tracking of gas displacement caused by water influx. While gravity measurements provide information on subsurface density changes, i.e., the composition of the reservoir, these data do only yield marginal information about temporal displacements of oil and inflowing water. We propose to complement gravimetric data with interferometric synthetic aperture radar surface deformation data to exploit the strong pressure deformation relationship for enhancing fluid flow direction forecasts. We have developed an ensemble Kalman‐filter‐based history matching framework for gas, gas condensate, and volatile oil reservoirs, which synergizes time‐lapse gravity and interferometric synthetic aperture radar data for improved reservoir management and reservoir forecasts. Based on a dual state–parameter estimation algorithm separating the estimation of static reservoir parameters from the dynamic reservoir parameters, our numerical experiments demonstrate that history matching gravity measurements allow monitoring the density changes caused by oil–gas phase transition and water influx to determine the saturation levels, whereas the interferometric synthetic aperture radar measurements help to improve the forecasts of hydrocarbon production and water displacement directions. The reservoir estimates resulting from the dual filtering scheme are on average 20%–40% better than those from the joint estimation scheme, but require about a 30% increase in computational cost.