Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

Cross-validation of active and passive microwave snowfall products over the continental United States

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’sCoreObservatorysensors and theCloudSatradar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radarcomposite product over the continental United States during the period from November 2014 to September 2020. Theanalysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPMCombined Radar Radiometer Algorithm (CORRA), theCloudSatSnow Profile product (2C-SNOW-PROFILE), and twopassive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI(SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the productsanalyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of thesnow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in theGMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall ratesby a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h21) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of theremote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by theconfounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers

A network suitable microwave radiometer for operational monitoring of the cloudy atmosphere.

Abstract The implementation of an operational network of microwave radiometers is presently hampered by the cost and complexity of the available instruments. For this reason, the definition and design of a low-cost microwave radiometer suitable for automatic, high-quality observations of liquid water path (LWP) were one objective of the BALTEX cloud liquid water network: CLIWA-NET. In the course of the project, it turned out that a full profiling radiometer with 14 channels can be produced at only about 30% higher cost than a classical dual-channel IWV/LWP radiometer. The profiling capability allows simultaneous observations of LWP and the lower tropospheric (0-5 km) humidity and temperature profiles with a temporal resolution of less than 10 s and a vertical resolution from 100 m to 1 km in the planetary boundary layer depending on height and atmospheric conditions. The latter is possible due to an elevation scan capability and by the implementation of a new filter bank design. The radiometer has several additional sensors (temperature, humidity, pressure, rain detector and GPS) which guarantee, together with a flexible software package, the operational performance of the system with maintenance intervals of about every 3 months. The performance of the first prototype has been verified during a 3-week campaign at Cabauw, The Netherlands.

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates.

Monitoring and profiling with CESAR Observatory

The climate system is complex. Although it is understood in qualitative terms, there are still many physical processes of which the impact on climate change is far from quantifi able. A well-known example of such a process is the interaction between cloud and rainfall formation, aerosols, radiation and the land-atmosphere energy exchange. It is one of the sources of large uncertainty in climate models

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Characterization of clouds and their radiative effects using ground-based instrumentation at a low-mountain site

The interaction of clouds with radiation and aerosols is the greatest source of uncertainty in future climate projections. Part of the reason is the limited amount of observations of clouds and hence the limited knowledge of cloud macro- and microphysical statistics in connection to their effects on the radiative budget and on the vertical redistribution of energy within the atmosphere. In 2007, the Atmospheric Radiation Measurement program�s (ARM) Mobile Facility (AMF) was operated for a nine-month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically-induced Precipitation Study (COPS). Based on the measurements of the AMF and COPS partner instrumentation, the present study aims at improving the data basis of cloud macro- and microphysical statistics and to assess the potential of the derived cloud properties to estimate the radiative effects of clouds. The synergy of various instruments is exploited to derive a data set of high quality thermodynamic and cloud property profiles with a temporal resolution of 30 s. While quality filters in the cloud microphysical retrieval techniques mostly affect the representativity of ice and mixed clouds in the data sample, water clouds are very well represented in the derived 364,850 atmospheric profiles. In total, clouds are present 72% of the time with multi-layer mixed phase (28.4%) and single-layer water clouds (11.3%) occurring most frequently. In order to evaluate the derived thermodynamic and cloud property profiles,radiative closure studies are performed with independent radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2.1% and 3.6% for the shortwave and longwave, respectively. In cloudy situations, differences, in particular in the shortwave, are much larger, but most of these can be related to broken cloud situations. The cloud radiative effect (CRE), i.e. the difference of cloudy and clear-sky net fluxes, has been analyzed for the whole nine-month period. The largest surface (SFC) net CRE has been found for multi-layer water (-110 Wm-2) and mixed clouds (-116 Wm-2). The estimated uncertainties in the modeled SFC and top of atmopshere (TOA) net CRE are up to 39% and 26%, respectively. For overcast, single-layer water clouds, sensitivity studies reveal that the SW CRE uncertainty at the SFC and TOA is likewise determined by uncertainties in liquid water path (LWP) and effective radius, if the LWP is larger than 100 gm-2. For low LWP values, uncertainties in SFC and TOA shortwave CRE are dominated by the uncertainty in LWP. Uncertainties in CRE due to uncertainties in the shape of the liquid water content (LWC) profile are typically smaller by a factor of two compared to LWP uncertainties. For the difference between the cloudy and clear-sky net heating rates, i.e. the cloud radiative forcing (CRF), of water clouds, the LWP and its vertical distribution within the cloud boundaries are the most important factors. In order to increase the accuracy of LWC profiles and consequentially of the estimates of CRE and CRF, advanced LWC retrieval techniques, such as the Integrated Profiling Technique (IPT), are needed. The accuracy of a LWC profile retrieval using typical microwave radiometer brightness temperatures and/or cloud radar reflectivities is investigated for two realistic cloud profiles. The interplay of the errors of the a priori profile, measurements and forward model on the retrieved LWC error and on the information content of the measurements is analyzed in detail. It is shown that the inclusion of the microwave radiometer observations in the LWC retrieval increases the number of degrees of freedom, i.e. the independent pieces of information in the measurements, by about 1 compared to a retrieval using measuremets from the cloud radar alone. Assuming realistic measurement and forward model errors, it is further demonstrated, that the error in the retrieved LWC is 60% or larger, if no a priori information is available, and that a priori information is essential for a better accuracy. The results of the present work strongly suggest to improve the LWC a priori profile and the corresponding error estimates in the IPT. However, there are few observational datasets available to construct accurate a priori profiles of LWC, and thus more observational data are needed to improve the knowledge of the a priori profile and the corresponding error covariance matrix

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version