The nominal facts and the October 1979 policy change

Business cycles ; Monetary policy ; Inflation (Finance)

Space Station: Leadership for the Future

No longer limited to occasional spectaculars, space has become an essential, almost commonplace dimension of national life. Among other things, space is an arena of competition with our allies and adversaries, a place of business, a field of research, and an avenue of cooperation with our allies. The space station will play a critical role in each of these endeavors. Perhaps the most significant feature of the space station, essential to its utility for science, commerce, and technology, is the permanent nature of its crew. The space station will build upon the tradition of employing new capabilities to explore further and question deeper, and by providing a permanent presence, the station should significantly increase the opportunities for conducting research in space. Economic productivity is, in part, a function of technical innovation. A major thrust of the station design effort is devoted to enhancing performance through advanced technology. The space station represents the commitment of the United States to a future in space. Perhaps most importantly, as recovery from the loss of Challenger and its crew continues, the space station symbolizes the national determination to remain undeterred by tragedy and to continue exploring the frontiers of space

Endogenous money supply and the business cycle

This paper documents changes in the cyclical behavior of nominal data series that appear after 1979:Q3 when the Federal Reserve implemented a policy to lower the inflation rate. Such changes were not apparent in real variables. A business cycle model with impulses to technology and a role for money is used to show how alternative money supply rules are expected to affect observed business cycle facts. In this model, changes in the money supply rules have almost no effect on the cyclical behavior of real variables, yet have a significant impact on the cyclical nature of nominal variables. Computational experiments with alternative policy rules suggest that the change in monetary policy in 1979 may account for the sort of instability observed in the U.S. data.Business cycles ; Money supply

The nominal facts and the October 1979 policy change

Gavin and Kydland (1999) calculated the cyclical properties of money and prices for the periods before and after the October 1979 policy change. In this article, we extend that work by adding four more years of data and including a study of nominal interest rates and inflation. The adoption of a disinflation policy in October 1979 does not appear to have had a measurable impact on the cyclical properties of real variables. However, it made a dramatic difference in the cyclical properties of nominal variables. We also examine the covariance structure of several nominal relationships: the autocovariance of inflation, the lag from money growth to inflation, and lag from money growth to nominal GDP growth. Generally, the monetary policy in the early period allowed the average inflation rate to ratchet upward with each business cycle. This policy was associated with high variances, high autocorrelations, and high cross-correlations among nominal variables. The moderate inflation policy followed in the second period was associated with lower mean growth rates, less volatility, and lower cross-correlations between money growth and inflation.Business cycles ; Monetary policy ; Inflation (Finance)

Graviton-photon conversion on spin 0 and 1/2 particles

The differential cross-sections for scattering of gravitons into photons on bosons and fermions are calculated in linearized quantum gravity. They are found to be strongly peaked in the forward direction and become constant at high energies. Numerically, they are very small as expected for such gravitational interactions.Comment: 13 pages, LaTeX with 5 figure

Advancing non-linear methods for coupled data assimilation across the atmosphere-land interface

In this thesis, I present two complementary frameworks to improve data assimila- tion in Earth system models, using the atmosphere-land interface as an exemplary case. As processes and components in the Earth system are coupled via interfaces, we would expect that assimilating observations from one Earth system component into another would improve the initialization of both components. In contrast to this expectation, it is often found that assimilation of atmospheric boundary layer observations into the land surface does not improve the analysis of the latter component. To disentangle the effects on the cross-compartmental assimilation, I take a step back from operational methods and use the coupled atmosphere-land modelling platform TerrSysMP in idealized twin experiments. I synthesize hourly and sparsely-distributed 2-metre-temperature observations from a single "nature" run. I subsequently assimilate these observations into the soil moisture with dif- ferent types of data assimilation methods. Based on this experimental structure, I test advanced data assimilation methods without model errors or biases. As my first framework, I propose to use localized ensemble Kalman filters for the unification of coupled data assimilation in Earth system models. To validate this framework, I conduct comparison experiments with a localized ensemble transform Kalman filter and a simplified extended Kalman filter, as similarly used at the ECMWF. Based on my developed environment, I find that we can assimilate 2-metre-temperature observations to improve the soil moisture analysis. In addition, hourly-updating the soil moisture with an ensemble Kalman filter decreases the error within the soil moisture analysis by up to 50 % compared to a daily-smoothing with a simplified extended Kalman filter. As a consequence, observations from the atmospheric boundary layer can be directly assimilated into the land surface model without a need of any intermediate interpolation, as normally used in land surface data assimilation. The improvement suggests that the land surface can be updated based on the same hourly cycle as used for mesoscale data assimilation. My results therefore prove that a unification of methods for data assimilation across the atmosphere-land interface is possible. As my second framework, I propose to use feature-based data assimilation to stabilize cross-compartmental data assimilation. To validate this framework, I use my implementation of an ensemble Kalman smoother that applies its analysis at the beginning of an assimilation window and resembles 4DEnVar. This smoother takes advantage of temporal dependencies in the atmosphere-land interface and improves the soil moisture analysis compared to the ensemble Kalman filter by 10 %. Subsequently based on this smoother, I introduce fingerprint operators as observational feature extractor into cross-compartmental data assimilation. These fingerprint operators take advantage of characteristic fingerprints in the difference between observations and model that point towards forecast errors, possibly in another Earth system component. As main finding, this concept can condense the information from the diurnal cycle in 2-metre-temperature observations into two observational features. This condensation makes the soil moisture analysis more robust against a miss-specified localization radius and errors in the observational covariance. Finally, I provide two new theoretical approaches to automatically learn such observational features with machine learning. In the first approach, I generalize ensemble Kalman filter with observational features to a novel kernelized ensemble transform Kalman filter.automatically This kernelized filter automatically con- structs the feature extractor on the basis of the given ensemble data and a chosen kernel function. In the second approach, I show that parameters within the data assimilation can be learned by variational Bayes. In this way, we can find whole distributions for parameters in data assimilation and, thus, determining their un- certainties. Furthermore, I prove the ensemble transform Kalman filter as a special solution of variational Bayes in the linearized-Gaussian case. These results suggest a possibility to specify the feature extractor as neural network and to train it with variational Bayes. These two approaches therefore prove that developments in machine learning can be used to extend data assimilation.In dieser Arbeit stelle ich zwei unterschiedliche Frameworks vor, um die Ini- tialisierung in gekoppelten Erdsystemmodellen für die Wettervorhersage zu verbessern. Dabei benutze ich die Schnittstelle zwischen der Atmosphäre und der Landoberfläche als Beispiel. Diese Schnittstelle bietet mir die Möglichkeit zu unter- suchen, in wie weit gekoppelte Datenassimilierung möglich ist. Prozesse und Kom- ponenten des Klimasystems sind über verschiedene Schnittstellen miteinander verbunden. Von daher würden wir erwarten, dass Beobachtungen aus der atmo- sphärischen Grenzschicht, auch die Initialisierung von Bodenmodellen verbessern, allerdings wurde in verschiedenen vorangegangenden Studien gezeigt, dass dies nicht der Fall ist. Um die Einflüsse von unterschiedlichen Fehler-Faktoren auf die Datenassimilierung zu reduzieren, benutze ich Experimente, die im Vergleich zur operationellen Wettervorhersage vereinfacht sind. Hierfür benutze ich das gekop- pelte Atmosphären-Land Vorhersagemodel TerrSysMP. All diese Experimente basieren auf einem Lauf ohne Datenassimilierung, den ich als meine "Natur" definiere. Aus diesem Naturlauf extrahiere ich künstliche 2-Meter-Temperatur Beobachtungen, welche dann mit unterschiedlichen Datenassimilierungsverfahren in die Bodenfeuchte assimiliert werden. Mit dieser Art von Experimenten teste ich fortschrittliche und nicht-lineare Datenassimilierungsverfahren für die Atmosphären- Land-Schnittstelle. Als erstes Framework schlage ich vor, einen lokalisierten Ensemble-Kalman-Filter für eine vereinheitlichte Datenassimilierung in Erdsystemmodellen zu verwenden. Um dieses Framework zu validieren, mache ich Vergleichsexperimente mit dem eben erwähnten lokalisierten Ensemble-Kalman-Filter und einem vereinfachten Extended-Kalman-Filter, der in ähnlicher Form beim Europäischen Zentrum für mittelfristige Wettervorhersage verwendet wird. Basierend auf meiner entwick- elten Umgebung zeige ich, dass 2-Meter-Temperatur Beobachtungen dafür ver- wendet werden können, um die Initialisierung der Bodenfeuchte zu verbessern. Der lokalisierte Ensemble-Kalman Filter reduziert zusätzlich den Fehler in der Ini- tialisierung der Bodenfeuchte um bis zu 50 %, im Vergleich zu dem vereinfachten Extended-Kalman-Filter. Dies zeigt zum ersten Mal, dass Beobachtungen aus der atmosphärischen Grenzschicht, direkt für die Initialisierung der Bodenfeuchte, ver- wendet werden können, ohne den Umweg einer Interpolierung zu nehmen, wie es bei dem vereinfachten Extended-Kalman-Filter der Fall ist. Darüberhinausge- hend legen diese Verbesserungen nahe, dass die Landoberfläche mit der gleichen stündlichen Aktualisierungs-Rate, wie die Atmosphäre, initialisiert werden kann. Deshalb beweisen diese Ergebnisse, dass eine vereinheitlichte Datenassimilierung über die Atmosphären-Land-Schnittstelle hinweg möglich ist. Als zweites Framework schlage ich vor, anstatt von Beobachtungen, Merkmale dieser Beobachtung zu assimilieren. Dies kann die Assimilierung, über die Atmosphären-Land Schnittstelle hinweg, verbessern. Um dieses Framework zu validieren, führe ich einen Ensemble-Kalman-Smoother ein. Dieser Ensemble- Kalman-Smoother initialisiert die Bodenfeuchte auf Basis eines Assimilierungs- fensters, ähnlich dem variationsgetriebenem vierdimensionellem Verfahren. Mit diesem Ensemble-Kalman-Smoother zeige ich, dass es möglich ist, zeitliche Ab- hängigkeiten innerhalb der Atmospähren-Land-Schnittstelle in der Datenassimi- lierung zu verwenden. Die Verwendung dieser Abhängigkeiten verbessert hierbei die Initialisierung der Bodenfeuchte. Auf Basis dieser Methodik, führe ich Oper- atoren ein, die Fingerabdrücke innerhalb von Beobachtungen ausnutzen. Diese Fingerabdruck-Operatoren nutze ich dafür, um Vorhersage-Fehler in anderen Komponenten des Erdsystems zu finden. Für die 2-Meter-Temperatur zeige ich, dass Informationen aus dem Tagesverlauf der Temperatur in 2 unterschiedliche Merkmale kondensiert werden können. Diese Kondensation macht die Initial- isierung der Bodenfeuchte robuster gegen Störungen innerhalb der Lokalisierung und der Beobachtungskovarianzen. Deshalb beweisen diese Ergebnisse, dass die eingeführten Fingerabdruck-Operatoren, die Datenassimilierung über die Atmosphären-Land Schnittstelle hinweg stabilisieren. Als letzten Punkte führe ich zwei neue, theoretische, Ansätze ein, um solche Beobachtungsmerkmale automatisch mit maschinellem Lernen zu finden. In meinem ersten Ansatz zeige ich, dass der merkmal-basierte Ensemble-Kalman- Filter unter dem Deckmantel des kernbasierten Ensemble-Transform-Kalman- Filter generalisiert werden kann. Hierbei lernt die Datenassimilierung automa- tisch die wichtigsten Beobachtungsmerkmale auf Basis der Ensemble Daten und einem gewählten Kern. In meinem zweiten Ansatz, zeige ich, dass Parameter des Ensemble-Kalman Filters mit variationsgetriebenen Bayesianischen Meth- oden erlernt werden können. Mit dieser Bayesianischen Methode kann die gesamte Wahrscheinlichkeitsverteilung der Parameter herausgefunden und so Unsicherheiten, innerhalb dieser, dargestellt werden können. Zusätzlich beweise ich, dass der Ensemble-Kalman-Filters eine spezielle Lösung dieses Ansatze im linear-Gaussischen Fall ist. Als Konsequenz, deute ich an, dass wir die Beobach- tungsmerkmale durch neuronale Netzwerke ersetzen können, die mit Hilfe dieses Ansatze erlernt werden. Von daher beweisen diese beiden Ansätze, dass Entwick- lungen im maschinellen Lernen dafür genutzt werden können, um Datenassimi- lierungsmethoden zu erweitern und möglicherweise zu verbessern

Wettability-independent droplet transport by \emph{Bendotaxis}

We demonstrate \textit{bendotaxis}, a novel mechanism for droplet self-transport at small scales. A combination of bending and capillarity in a thin channel causes a pressure gradient that, in turn, results in the spontaneous movement of a liquid droplet. Surprisingly, the direction of this motion is always the same, regardless of the wettability of the channel. We use a combination of experiments at a macroscopic scale and a simple mathematical model to study this motion, focussing in particular on the time scale associated with the motion. We suggest that \emph{bendotaxis} may be a useful means of transporting droplets in technological applications, for example in developing self-cleaning surfaces, and discuss the implications of our results for such applications.Comment: 5 pages, 4 figures. Supplementary Information available on reques

Inflation persistence and flexible prices

If the central bank follows an interest rate rule, then inflation is likely to be persistence, even when prices are fully flexible. Any shock, whether persistent or not, may lead to inflation persistence. In equilibrium, the dynamics of inflation are determined by the evolution of the spread between the real interest rate and the central bank’s target. Inflation persistence in U.S. data can be characterized by a vector autocorrelation function relating inflation and deviations of output from trend. This paper shows that a flexible-price general equilibrium business cycle model with money and a central bank using an interest rate target can account for such inflation persistence.Inflation (Finance) ; Econometric models ; Taylor's rule

Cold gas in the inner regions of intermediate redshift clusters

Determining gas content and star formation rate has known remarkable progress in field galaxies, but has been much less investigated in galaxies inside clusters. We present the first CO observations of luminous infrared galaxies (LIRGs) inside the virial radii of two intermediate redshift clusters, CL1416+4446 (z=0.397) and CL0926+1242 (z=0.489). We detect three galaxies at high significance (5 to 10 sigma), and provide robust estimates of their CO luminosities, L'CO. In order to put our results into a general context, we revisit the relation between cold and hot gas and stellar mass in nearby field and cluster galaxies. We find evidence that at fixed LIR (or fixed stellar mass), the frequency of high L'CO galaxies is lower in clusters than in the field, suggesting environmental depletion of the reservoir of cold gas. The level of star formation activity in a galaxy is primarily linked to the amount of cold gas, rather than to the galaxy mass or the lookback time. In clusters, just as in the field, the conversion between gas and stars seems universal. The relation between LIR and L'CO for distant cluster galaxies extends the relation of nearby galaxies to higher IR luminosities. Nevertheless, the intermediate redshift galaxies fall well within the dispersion of the trend defined by local systems. Considering that L'CO is generally derived from the CO(1-0) line and sensitive to the vast majority of the molecular gas in the cold interstellar medium of galaxies, but less to the part which will actually be used to form stars, we suggest that molecular gas can be stripped before the star formation rate is affected. Combining the sample of Geach et al. (2009, 2011) and ours, we find evidence for a decrease in CO towards the cluster centers. This is the first hint of an environmental impact on cold gas at intermediate redshift.Comment: Accepted for publication in Astronomy and Astrophysic