scholarly journals Quantifying EOS Aqua and NOAA POES AMSU-A Brightness Temperature Biases for Weather and Climate Applications Utilizing the SNO Method

2007 ◽  
Vol 24 (11) ◽  
pp. 1895-1909 ◽  
Author(s):  
Robert A. Iacovazzi ◽  
Changyong Cao
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Microwave Frequency ◽  
Low Noise ◽  
Cold Bias ◽  
Earth Observing System ◽  
Weather And Climate ◽  
Ensemble Mean ◽  
Systematic Biases ◽  
The Individual

Abstract Systematic biases between brightness temperature (Tb) measurements made from concurrently operational Advanced Microwave Sounding Unit-A (AMSU-A) instruments can introduce errors into weather and climate applications. For this reason, in this study the ability of the simultaneous nadir overpass (SNO) method to estimate relative Tb biases between operational Earth Observing System (EOS) Aqua and Polar-orbiting Operational Environmental Satellites (POES) NOAA-15, NOAA-16, and NOAA-18 AMSU-A instruments is evaluated. From an analysis of SNO events occurring from 21 May 2005 to 31 July 2006, AMSU-A SNO-ensemble mean Tb biases could not be statistically determined for window channels, while significant bias detection to within about 0.02 K is accomplished in some low-noise sounding channels. These results are shown to be a consequence of the decrease of the earth-scene Tb variability with increasing atmospheric zenith opacity, which is a function of microwave frequency. Examination of SNO-ensemble mean Tb biases for two independent AMSU-A instrument components—AMSU-A1–1 and AMSU-A1–2—exposed a significant cold (warm) bias on the order of 0.4 K (0.2 K) in the AMSU-A1–1 unit on board the NOAA-18 (Aqua) satellite. This analysis also revealed on average a significant cold bias on the order of 0.1 K in the NOAA-16 AMSU-A1–2 component. Furthermore, the individual SNO mean Tb biases were often found to be a function of the SNO earth-scene average Tb, which is a manifestation of instrument calibration errors. On the other hand, it was found that determining the root cause of such errors is inhibited by the lack of postlaunch quality control of the AMSU-A calibration-related hardware. Based on the results of this study, a need to reduce impacts of surface emissivity and temperature inhomogeneities on the SNO method in microwave radiometer window channels becomes evident. In addition, the unparalleled ability of the SNO method to isolate and quantify intersatellite, instrument-related Tb biases is demonstrated in most sounding channels, which is necessary to improve weather and climate applications.

scholarly journals Analysis of Ensemble Mean Forecasts: The Blessings of High Dimensionality

2019 ◽  
Vol 147 (5) ◽  
pp. 1699-1712 ◽  
Author(s):  
Bo Christiansen
Keyword(s):  
High Dimensional ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Analytical Results ◽  
Weather And Climate ◽  
Ensemble Mean ◽  
The Individual ◽  
Almost All ◽  
Community Standard ◽  
Better Than

Abstract In weather and climate sciences ensemble forecasts have become an acknowledged community standard. It is often found that the ensemble mean not only has a low error relative to the typical error of the ensemble members but also that it outperforms all the individual ensemble members. We analyze ensemble simulations based on a simple statistical model that allows for bias and that has different variances for observations and the model ensemble. Using generic simplifying geometric properties of high-dimensional spaces we obtain analytical results for the error of the ensemble mean. These results include a closed form for the rank of the ensemble mean among the ensemble members and depend on two quantities: the ensemble variance and the bias both normalized with the variance of observations. The analytical results are used to analyze the GEFS reforecast where the variances and bias depend on lead time. For intermediate lead times between 20 and 100 h the two terms are both around 0.5 and the ensemble mean is only slightly better than individual ensemble members. For lead times larger than 240 h the variance term is close to 1 and the bias term is near 0.5. For these lead times the ensemble mean outperforms almost all individual ensemble members and its relative error comes close to −30%. These results are in excellent agreement with the theory. The simplifying properties of high-dimensional spaces can be applied not only to the ensemble mean but also to, for example, the ensemble spread.

scholarly journals A compact low-noise frontend for interleaved Rx/Tx arrays at K-/Ka-Band

2021 ◽  
pp. 1-7
Author(s):  
Anton Sieganschin ◽  
Thomas Jaschke ◽  
Arne F. Jacob
Keyword(s):  
Insertion Loss ◽  
Printed Circuit Board ◽  
Noise Figure ◽  
Low Noise ◽  
Single Band ◽  
Circuit Board ◽  
Dual Band ◽  
Ka Band ◽  
Compression Properties ◽  
The Individual

Abstract This contribution deals with a frontend for interleaved receive (Rx)-/transmit (Tx)-integrated phased arrays at K-/Ka-band. The circuit is realized in printed circuit board technology and feeds dual-band Rx/Tx- and single-band Tx-antenna elements. The dual-band element feed is composed of a substrate-integrated waveguide (SIW) diplexer with low insertion loss, a low-noise amplifier (LNA), a bandpass filter, and several passive transitions. The compression properties of the LNA are identified through two-tone measurements. The results dictate the maximum allowable output power of the power amplifier. The single band feed consists of a SIW with several transitions. Simulation and measurement results of the individual components are presented. The frontend is assembled and measured. It exhibits an Rx noise figure of 2 dB, a Tx insertion loss of ~ 2.9 dB, and an Rx/Tx-isolation of 70 dB. The setup represents the unit cell of a full array and thus complies with the required half-wave spacing at both Rx and Tx.

INFLUENCE OF REGULARITY OF SATELLITE MICROWAVE RADIOMETER MEASUREMENTS ON THE ACCURACY OF BRIGHTNESS TEMPERATURE REPRODUCTION IN AREAS OF THE TROPICAL CYCLONES

2021 ◽  
pp. 78-85
Author(s):  
А. G. Grankov ◽  
◽  
А. А. Milshin ◽  
Keyword(s):  
Brightness Temperature ◽  
Tropical Cyclones ◽  
Microwave Radiometer ◽  
Measurement Data ◽  
Hurricane Wilma ◽  
Brightness Temperatures ◽  
Atmosphere System ◽  
Ocean Atmosphere ◽  
The Caribbean ◽  
Satellite Microwave

An accuracy of reproduction of daily variations in the ocean–atmosphere system brightness temperature in the areas of development and movement of tropical hurricanes in the Caribbean Sea and Gulf of Mexico is analyzed. The analysis is based on the data of single and group satellite microwave radiometer measurements. The results are obtained using archival measurement data of SSM/I radiometers from the F11, F13, F14, and F15 DMSP satellites during the period of existence of tropical hurricanes Bret and Wilma. An example is given to demonstrate the use of daily brightness temperatures obtained from DMSP satellites for monitoring the development and propagation of hurricane Wilma.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Improving weather and climate predictions by training of supermodels

2019 ◽  
Author(s):  
Francine Schevenhoven ◽  
Frank Selten ◽  
Alberto Carrassi ◽  
Noel Keenlyside
Keyword(s):  
Time Derivative ◽  
Learning Rule ◽  
Training Methods ◽  
Model Errors ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Present Context ◽  
Training Schemes ◽  
The Individual ◽  
Derivatives Of

Abstract. Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so called "supermodel". Here we focus on the weighted supermodel – the supermodel's time derivative is a weighted superposition of the time-derivatives of the imperfect models, referred to as weighted supermodeling. A crucial step is to train the weights of the supermodel on the basis of historical observations. Here we apply two different training methods to a supermodel of up to four different versions of the global atmosphere-ocean-land model SPEEDO. The standard version is regarded as truth. The first training method is based on an idea called Cross Pollination in Time (CPT), where models exchange states during the training. The second method is a synchronization based learning rule, originally developed for parameter estimation. We demonstrate that both training methods yield climate simulations and weather predictions of superior quality as compared to the individual model versions. Supermodel predictions also outperform predictions based on the commonly used Multi-Model Ensemble (MME) mean. Furthermore we find evidence that negative weights can improve predictions in cases where model errors do not cancel (for instance all models are warm with respect to the truth). In principle the proposed training schemes are applicable to state-of-the-art models and historical observations. A prime advantage of the proposed training schemes is that in the present context relatively short training periods suffice to find good solutions. Additional work needs to be done to assess the limitations due to incomplete and noisy data, to combine models that are structurally different (different resolution and state representation for instance) and to evaluate cases for which the truth falls outside of the model class.

scholarly journals An efficient training scheme for supermodels

2017 ◽  
Vol 8 (2) ◽  
pp. 429-438 ◽  
Author(s):  
Francine J. Schevenhoven ◽  
Frank M. Selten
Keyword(s):  
Climate Models ◽  
State Of The Art ◽  
Atmospheric Model ◽  
High Dimensional ◽  
Model Errors ◽  
Global Atmospheric Model ◽  
Training Scheme ◽  
Skill Scores ◽  
Weather And Climate ◽  
The Individual

Abstract. Weather and climate models have improved steadily over time as witnessed by objective skill scores, although significant model errors remain. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called supermodel. In this paper a new training scheme to construct such a supermodel is explored using a technique called cross pollination in time (CPT). In the CPT approach the models exchange states during the prediction. The number of possible predictions grows quickly with time, and a strategy to retain only a small number of predictions, called pruning, needs to be developed. The method is explored using low-order dynamical systems and applied to a global atmospheric model. The results indicate that the CPT training is efficient and leads to a supermodel with improved forecast quality as compared to the individual models. Due to its computational efficiency, the technique is suited for application to state-of-the art high-dimensional weather and climate models.

scholarly journals Passive Microwave Melt Onset Retrieval Based on a Variable Threshold: Assessment in the Canadian Arctic Archipelago

2019 ◽  
Vol 11 (11) ◽  
pp. 1304 ◽  
Author(s):  
Stephen Marshall ◽  
K. Andrea Scott ◽  
Randall K. Scharien
Keyword(s):  
Brightness Temperature ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Canadian Arctic ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Canadian Arctic Archipelago ◽  
Variable Threshold ◽  
Resolution Data

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

scholarly journals Investigation of the possibility of increasing the sensitivity of a microwave radiometer

2019 ◽  
Vol 30 ◽  
pp. 13007
Author(s):  
Alexander Luchinin ◽  
Ivan Malygin
Keyword(s):  
Microwave Radiometer ◽  
Sensitivity Study ◽  
Low Noise ◽  
Low Noise Amplifier ◽  
Central Frequency ◽  
Noise Amplifier

The results of a sensitivity study of various types of quadratic detectors for a microwave radiometer are presented, both with and without a low-noise amplifier. A low-noise amplifier with a gain of 60 dB and a bandwidth of 1 GHz at a central frequency of 4 GHz was developed and studied.

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